#reading spss file 
import pyreadstat as prs
#data manipulation
import numpy as np
import pandas as pd
#data visualization
import matplotlib.pyplot as plt  
import seaborn as sns
import plotly.express as px
import plotly.io as pio
import plotly.graph_objects as go
pio.renderers.default = "notebook"
#data preprocessing
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
#goefileloade/read
import geopandas as gpd
import json
#pandas settings  
pd.set_option('display.max_columns', None)
pd.set_option('future.no_silent_downcasting', True)

#getting the data from the sav file
df, meta = prs.read_sav(r"C:\Users\ULTRAPC\Desktop\Final project\Project_Dataset.sav")

df

#setting up the variable view
variable_view = pd.DataFrame({'Column' : meta.column_names, 
            'Label': meta.column_labels , 
            'Value' : [meta.variable_value_labels.get(var,{}) for var in meta.column_names]
            })

variable_view

#setting up the labeled_df
labeled_df = df.copy()
for column, value in meta.variable_value_labels.items():
    if column in labeled_df.columns:
        labeled_df[column] = labeled_df[column].map(value).fillna(labeled_df[column])

labeled_df

variable_view[variable_view['Label'].str.contains('Feeling')]

variable_view[variable_view['Label'].str.contains('Feeling')].values

array([['Q46', 'Feeling of happiness',
        {-5.0: 'Other missing; Multiple answers Mail (EVS)', -4.0: 'Not asked', -2.0: 'No answer', -1.0: "Don't know", 1.0: 'Very happy', 2.0: 'Quite happy', 3.0: 'Not very happy', 4.0: 'Not at all happy'}]],
      dtype=object)

print(variable_view[variable_view['Label'].str.contains('education')].values[2])

['Q275' 'Highest educational level: Respondent [ISCED 2011]'
 {-5.0: 'Other missing; Multiple answers Mail (EVS)', -4.0: 'Not asked', -3.0: 'Not applicable', -2.0: 'No answer', -1.0: "Don't know", 0.0: 'Early childhood education (ISCED 0) / no education', 1.0: 'Primary education (ISCED 1)', 2.0: 'Lower secondary education (ISCED 2)', 3.0: 'Upper secondary education (ISCED 3)', 4.0: 'Post-secondary non-tertiary education (ISCED 4)', 5.0: 'Short-cycle tertiary education (ISCED 5)', 6.0: 'Bachelor or equivalent (ISCED 6)', 7.0: 'Master or equivalent (ISCED 7)', 8.0: 'Doctoral or equivalent (ISCED 8)'}]

print(variable_view[variable_view['Label'].str.contains('satisfaction', case=False)].values[1])

['Q50' 'Satisfaction with financial situation of household'
 {-5.0: 'Missing; Unknown', -4.0: 'Not asked', -2.0: 'No answer', -1.0: "Don't know", 1.0: 'Dissatisfied', 2.0: '2', 3.0: '3', 4.0: '4', 5.0: '5', 6.0: '6', 7.0: '7', 8.0: '8', 9.0: '9', 10.0: 'Satisfied'}]

#Frequency of the variable 'Feeling of happiness Q46'

DataFrame = pd.DataFrame({
    'Feeling of happiness (LV_Q46)': labeled_df['Q46'],
    'Feeling of happiness (NV_Q46)': df['Q46'],
    'Highest educational level: Respondent [ISCED 2011] (LV_Q275)': labeled_df['Q275'],
    'Highest educational level: Respondent [ISCED 2011] (NV_Q275)': df['Q275'],
    'Satisfaction with financial situation of household (LV_Q50)': labeled_df['Q50'],
    'Satisfaction with financial situation of household (NV_Q50)': df['Q50'],
})

DataFrame

#frequency of the variable 'Feeling of happiness Q46'
Frequency_of_happiness = pd.DataFrame({
    'Frequency': DataFrame['Feeling of happiness (LV_Q46)'].value_counts().sort_index(),
    'Cumulative Frequency': DataFrame['Feeling of happiness (LV_Q46)'].value_counts().sort_index().cumsum(),
    'Percentage': (DataFrame['Feeling of happiness (LV_Q46)'].value_counts(normalize=True)*100).sort_index().round(2),
    'Cumulative Percentage': (DataFrame['Feeling of happiness (LV_Q46)'].value_counts(normalize=True).sort_index().cumsum()*100),
}).reset_index()
Frequency_of_happiness = Frequency_of_happiness.merge(pd.DataFrame(DataFrame[['Feeling of happiness (NV_Q46)', 'Feeling of happiness (LV_Q46)']].value_counts().sort_index())
.reset_index(), on='Feeling of happiness (LV_Q46)', how='left').drop(columns='count')
reorder = ['Feeling of happiness (LV_Q46)', 'Feeling of happiness (NV_Q46)', 'Frequency', 'Cumulative Frequency',  'Percentage','Cumulative Percentage']
Frequency_of_happiness = Frequency_of_happiness[reorder]

Frequency_of_happiness

#plot
fig = px.bar(
    data_frame= Frequency_of_happiness,
    x= 'Feeling of happiness (LV_Q46)',
    y= 'Frequency',
    title= 'Frequency of the variable "Feeling of happiness Q46"',
    hover_data={'Percentage':True}
)

fig.update_layout(
    xaxis=dict(
        categoryorder='array', 
        categoryarray=['Early childhood education (ISCED 0) / no education', 'Primary education (ISCED 1)', 'Lower secondary education (ISCED 2)', 'Upper secondary education (ISCED 3)',
                    'Post-secondary non-tertiary education (ISCED 4)', 'Short-cycle tertiary education (ISCED 5)', 'Bachelor or equivalent (ISCED 6)',
                    'Master or equivalent (ISCED 7)', 'Doctoral or equivalent (ISCED 8)'],
    ),
    plot_bgcolor='rgba(240, 240, 240, 1)',  # Light background color
    title_font=dict(size=24, family='Arial', color='black'),  # Title font styling
    xaxis_title='Frequency',
    yaxis_title='Feeling of happiness',
    legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)

fig.show()

# frequency of the variable 'Highest educational level: Respondent's Spouse [ISCED 2011]'
Frequency_of_education = pd.DataFrame({
    'Frequency': DataFrame['Highest educational level: Respondent [ISCED 2011] (LV_Q275)'].value_counts().sort_index(),
    'Cumulative Frequency': DataFrame['Highest educational level: Respondent [ISCED 2011] (LV_Q275)'].value_counts().sort_index().cumsum(),
    'Percentage': (DataFrame['Highest educational level: Respondent [ISCED 2011] (LV_Q275)'].value_counts(normalize=True)*100).sort_index().round(2),
    'Cumulative Percentage': (DataFrame['Highest educational level: Respondent [ISCED 2011] (LV_Q275)'].value_counts(normalize=True).sort_index().cumsum()*100).round(2),
}).reset_index()

Frequency_of_education

# plot
# Creating a bar chart with hover data
fig = px.bar(
    Frequency_of_education,
    x="Highest educational level: Respondent [ISCED 2011] (LV_Q275)",
    y="Frequency",
    hover_data={"Percentage": True},
    title='Frequency of the variable "Highest educational level: Respondent [ISCED 2011]"',
)

fig.update_layout(
    xaxis=dict(
        categoryorder='array', 
        categoryarray=['Early childhood education (ISCED 0) / no education', 'Primary education (ISCED 1)', 'Lower secondary education (ISCED 2)', 'Upper secondary education (ISCED 3)',
                    'Post-secondary non-tertiary education (ISCED 4)', 'Short-cycle tertiary education (ISCED 5)', 'Bachelor or equivalent (ISCED 6)',
                    'Master or equivalent (ISCED 7)', 'Doctoral or equivalent (ISCED 8)'],
    ),
    plot_bgcolor='rgba(240, 240, 240, 1)',  # Light background color
    title_font=dict(size=24, family='Arial', color='black'),  # Title font styling
    xaxis_title='Frequency',
    yaxis_title='Highest educational level: Respondent',
    legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)

fig.show()

#frequency of the variable 'Satisfaction with financial situation of household'
Frequency_of_satisfaction = pd.DataFrame({
    'Frequency': labeled_df['Q50'].value_counts(),
    'Cumulative Frequency': DataFrame['Satisfaction with financial situation of household (LV_Q50)'].value_counts().sort_index().cumsum(),
    'Percentage': (DataFrame['Satisfaction with financial situation of household (LV_Q50)'].value_counts(normalize=True)*100).sort_index().round(2),
    'Cumulative Percentage': (DataFrame['Satisfaction with financial situation of household (LV_Q50)'].value_counts(normalize=True).sort_index().cumsum()*100),
}).reset_index()

Frequency_of_satisfaction['index'] = Frequency_of_satisfaction['index'].map({'Dissatisfied': 1 , '2':2, '3':3, '4':4, '5':5, '6':6, '7':7, '8':8, '9':9, 'Satisfied':10})
Frequency_of_satisfaction.rename(columns={'index': 'Satisfaction with financial situation of household'}, inplace=True)

Frequency_of_satisfaction

# plot
# Creating a bar chart with hover data
fig = px.bar(
    Frequency_of_satisfaction,
    x='Satisfaction with financial situation of household',
    y="Frequency",
    hover_data={"Percentage": True},
    title='Frequency of the variable "satisfaction with financial situation of household"',
)

fig.update_layout(
    xaxis=dict(
        categoryorder='array', 
        categoryarray=['1', '2', '3', '4',
                    '5', '6', '7',
                    '8', '9', '10'],
        tickmode='array',
        tickvals=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],  # Ensure all x-axis values are shown
    ),
    plot_bgcolor='rgba(240, 240, 240, 1)',  # Light background color
    title_font=dict(size=24, family='Arial', color='black'),  # Title font styling
    xaxis_title='Frequency',
    yaxis_title='satisfaction with financial situation of household',
    legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)

fig.show()

# Create a violing plot
fig = px.violin(
    DataFrame,
    y= 'Highest educational level: Respondent [ISCED 2011] (LV_Q275)',
    x= 'Feeling of happiness (LV_Q46)',
    box=True,  # Add boxplot overlay
    points='all',  # Show all data points
    title="The Highest educational level based on Feeling of happiness",
)

# Update the order of categories
fig.update_layout(
    xaxis=dict(
        categoryorder='array', 
        categoryarray=['Not at all happy', 'Not very happy', 'Quite happy', 'Very happy']
    ),
    yaxis=dict(
        categoryorder='array', 
        categoryarray=['Early childhood education (ISCED 0) / no education', 'Primary education (ISCED 1)', 'Lower secondary education (ISCED 2)', 'Upper secondary education (ISCED 3)',
                    'Post-secondary non-tertiary education (ISCED 4)', 'Short-cycle tertiary education (ISCED 5)', 'Bachelor or equivalent (ISCED 6)',
                    'Master or equivalent (ISCED 7)', 'Doctoral or equivalent (ISCED 8)'],
    ),
    plot_bgcolor='rgba(240, 240, 240, 1)',  # Light background color
    title_font=dict(size=24, family='Arial', color='black'),  # Title font styling
    xaxis_title='Feeling of happiness',
    yaxis_title='Highest educational level: Respondent [ISCED 2011]',
    legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)

# Update the violins for styling
fig.update_traces(
    width=0.5,  # Thinner violins
    line_color='black',  # Black outline
    meanline_visible=True,  # Show mean line
    scalemode='count',  # Adjust width by the count of observations
    fillcolor='rgba(0,0,0,0)',  # Remove fill
)



# Show the plot
fig.show()

# Create a violing plot
fig = px.violin(
    DataFrame,
    y= 'Satisfaction with financial situation of household (NV_Q50)',
    x= 'Feeling of happiness (LV_Q46)',
    box=True,  # Add boxplot overlay
    points='all',  # Show all data points
    title="The value of Satisfaction with financial situation of household based on Feeling of happiness",
)

# Update the order of categories
fig.update_layout(
    xaxis=dict(
        categoryorder='array', 
        categoryarray=['Not at all happy', 'Not very happy', 'Quite happy', 'Very happy']
    ),
    plot_bgcolor='rgba(240, 240, 240, 1)',  # Light background color
    title_font=dict(size=24, family='Arial', color='black'),  # Title font styling
    xaxis_title='Feeling of happiness',
    yaxis_title='Satisfaction with financial situation of household',
    legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)

# Update the violins for styling
fig.update_traces(
    width=0.5,  # Thinner violins
    line_color='black',  # Black outline
    meanline_visible=True,  # Show mean line
    scalemode='count',  # Adjust width by the count of observations
    fillcolor='rgba(0,0,0,0)',  # Remove fill
)

# Show the plot
fig.show()

#correlation matrix
correlation_matrix = DataFrame[['Feeling of happiness (NV_Q46)','Highest educational level: Respondent [ISCED 2011] (NV_Q275)','Satisfaction with financial situation of household (NV_Q50)']].corr()
correlation_matrix

# checking the scale
DataFrame.groupby(['Feeling of happiness (LV_Q46)'])['Feeling of happiness (NV_Q46)'].value_counts().reset_index()

#creating a function for reversed scale
df['inverse_Q46'] = df['Q46'].map({1:4,2:3,3:2,4:1})
DataFrame['Feeling of happiness (NV_Q46)_inverse'] = DataFrame['Feeling of happiness (NV_Q46)'].map({1:4,2:3,3:2,4:1})

DataFrame

#correlation matrix
correlation_matrix = DataFrame[['Feeling of happiness (NV_Q46)_inverse','Highest educational level: Respondent [ISCED 2011] (NV_Q275)','Satisfaction with financial situation of household (NV_Q50)']].corr(method='spearman')
correlation_matrix

# Renaming the correlation matrix
correlation_matrix=correlation_matrix.rename({'Feeling of happiness (NV_Q46)_inverse': 'Feeling of happiness',
                            'Highest educational level: Respondent [ISCED 2011] (NV_Q275)':'Highest educational level',
                            'Satisfaction with financial situation of household (NV_Q50)': 'Satisfaction with financial situation of household'},
                            ).rename(columns={'Feeling of happiness (NV_Q46)_inverse': 'Feeling of happiness',
                            'Highest educational level: Respondent [ISCED 2011] (NV_Q275)':'Highest educational level',
                            'Satisfaction with financial situation of household (NV_Q50)': 'Satisfaction with financial situation of household'})
correlation_matrix

# Create a heatmap to visualize the correlations
px.imshow(correlation_matrix,
        text_auto=True,
        color_continuous_scale='viridis')

# df correlation table
numeric_df = df.select_dtypes(include=['number'])
correlation_matrix_df = numeric_df.corr(method='spearman')
correlation_matrix_df.dropna(axis=1, how='all', inplace=True)
correlation_matrix_df.dropna(axis=0, how='all', inplace=True)
correlation_matrix_df = correlation_matrix_df.drop(['D_INTERVIEW','J_INTDATE','K_TIME_START','K_TIME_END','K_DURATION','N_REGION_ISO','N_REGION_WVS','N_TOWN','G_TOWNSIZE','G_TOWNSIZE2'], axis=0)
correlation_matrix_df = correlation_matrix_df.drop(['D_INTERVIEW','J_INTDATE','K_TIME_START','K_TIME_END','K_DURATION','N_REGION_ISO','N_REGION_WVS','N_TOWN','G_TOWNSIZE','G_TOWNSIZE2'], axis=1)
correlation_matrix_df

fig = px.imshow(correlation_matrix_df,
                text_auto=True)
fig

#pio.write_html(fig, file='plot.html', auto_open=True)  #Save and open the plot in a browser

corrQ46 = correlation_matrix_df.loc['inverse_Q46'].dropna().reset_index()
corrQ50 = correlation_matrix_df.loc['Q50'].dropna().reset_index()

corrQ46[corrQ46['inverse_Q46'] >= 0.2].sort_values('inverse_Q46', ascending=False)

corrQ50[corrQ50['Q50'] >= 0.2].sort_values('Q50', ascending=False)

variable_view[variable_view['Column'] == 'Q48'].values

array([['Q48', 'How much freedom of choice and control',
        {-5.0: 'Other missing; Multiple answers Mail (EVS)', -4.0: 'Not asked', -2.0: 'No answer', -1.0: "Don't know", 1.0: 'None at all', 2.0: '2', 3.0: '3', 4.0: '4', 5.0: '5', 6.0: '6', 7.0: '7', 8.0: '8', 9.0: '9', 10.0: 'A great deal'}]],
      dtype=object)

y = DataFrame['Feeling of happiness (NV_Q46)_inverse']
X = DataFrame[['Satisfaction with financial situation of household (NV_Q50)','Highest educational level: Respondent [ISCED 2011] (NV_Q275)']]
X = sm.add_constant(X)
model = sm.OLS(y, X).fit()
print(model.summary())

                                      OLS Regression Results                                     
=================================================================================================
Dep. Variable:     Feeling of happiness (NV_Q46)_inverse   R-squared:                       0.193
Model:                                               OLS   Adj. R-squared:                  0.192
Method:                                    Least Squares   F-statistic:                     143.4
Date:                                   Wed, 12 Feb 2025   Prob (F-statistic):           1.44e-56
Time:                                           23:27:39   Log-Likelihood:                -890.39
No. Observations:                                   1200   AIC:                             1787.
Df Residuals:                                       1197   BIC:                             1802.
Df Model:                                              2                                         
Covariance Type:                               nonrobust                                         
================================================================================================================================
                                                                   coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------------------------------
const                                                            2.1882      0.050     44.038      0.000       2.091       2.286
Satisfaction with financial situation of household (NV_Q50)      0.1177      0.007     16.936      0.000       0.104       0.131
Highest educational level: Respondent [ISCED 2011] (NV_Q275)     0.0083      0.007      1.224      0.221      -0.005       0.022
==============================================================================
Omnibus:                       14.789   Durbin-Watson:                   1.764
Prob(Omnibus):                  0.001   Jarque-Bera (JB):               22.964
Skew:                          -0.071   Prob(JB):                     1.03e-05
Kurtosis:                       3.663   Cond. No.                         24.2
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

# Predecting y values
DataFrame['y_pred'] = model.predict(X)

# Create grid for regression plane
x1_range = np.linspace(DataFrame['Satisfaction with financial situation of household (NV_Q50)'].min(), 
                    DataFrame['Satisfaction with financial situation of household (NV_Q50)'].max(), 10)
x2_range = np.linspace(DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'].min(), 
                    DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'].max(), 10)

x1_grid, x2_grid = np.meshgrid(x1_range, x2_range)
x1_flat = x1_grid.flatten()
x2_flat = x2_grid.flatten()

# Prepare the grid for prediction
grid_data = pd.DataFrame({
    'const': 1,
    'Satisfaction with financial situation of household (NV_Q50)': x1_flat,
    'Highest educational level: Respondent [ISCED 2011] (NV_Q275)': x2_flat
})

# Predict values for the regression plane
y_grid = model.predict(grid_data)
y_grid = y_grid.values.reshape(x1_grid.shape)

# Visualize using Plotly
fig = go.Figure()

# Add actual data points
fig.add_trace(go.Scatter3d(
    x=DataFrame['Satisfaction with financial situation of household (NV_Q50)'], 
    y=DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'], 
    z=DataFrame['Feeling of happiness (NV_Q46)_inverse'],
    mode='markers',
    marker=dict(size=5, color='blue'),
    name='Actual',
    hovertemplate=(
        'Satisfaction: %{x}<br>'
        'Education Level: %{y}<br>'
        'Happiness: %{z}<br>'
    )
))

# Add predicted data points
fig.add_trace(go.Scatter3d(
    x=DataFrame['Satisfaction with financial situation of household (NV_Q50)'], 
    y=DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'], 
    z=DataFrame['y_pred'],
    mode='markers',
    marker=dict(size=5, color='red'),
    name='Predicted',
    hovertemplate=(
        'Satisfaction: %{x}<br>'
        'Education Level: %{y}<br>'
        'Predicted Happiness: %{z}<br>'
    )
))

# Add regression plane
fig.add_trace(go.Surface(
    x=x1_grid, 
    y=x2_grid, 
    z=y_grid, 
    colorscale='Viridis',
    opacity=0.5,
    name='Regression Plane',
    hovertemplate=(
        'Satisfaction: %{x}<br>'
        'Education Level: %{y}<br>'
        'Plane Value: %{z}<br>'
    )
))

# Customize layout to show all values on the axis
fig.update_layout(
    scene=dict(
        xaxis=dict(
            title='Satisfaction with financial situation',
            range=[DataFrame['Satisfaction with financial situation of household (NV_Q50)'].min(),
                DataFrame['Satisfaction with financial situation of household (NV_Q50)'].max()],
            tickangle=-30               # Rotate ticks slightly for better readability
        ),
        yaxis=dict(
            title='Education Level (ISCED)',
            range=[DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'].min(),
                DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'].max()],
            tickangle=-30               # Rotate ticks slightly
        ),
        zaxis=dict(
            title='Happiness',
            range=[DataFrame['Feeling of happiness (NV_Q46)_inverse'].min(),
                DataFrame['Feeling of happiness (NV_Q46)_inverse'].max()]
        )
    ),
    title='Multilinear Regression',
    margin=dict(l=50, r=50, b=50, t=50),  # Adjust margins for cleaner layout
    legend=dict(
        orientation="h",  # Horizontal legend layout
        yanchor="bottom",  # Anchor to the bottom of the plot
        y=-0.2,            # Position slightly below the plot
        xanchor="center",  # Center the legend
        x=0.5              # Align horizontally at the center
    )
)

fig.show()

#pio.write_html(fig, file='plot.html', auto_open=True)

DataFrame['How much freedom of choice and control (Q48)'] = df['Q48']

y = DataFrame['Feeling of happiness (NV_Q46)_inverse']
X = DataFrame[['Satisfaction with financial situation of household (NV_Q50)','How much freedom of choice and control (Q48)']]
X = sm.add_constant(X)
model = sm.OLS(y, X).fit()
print(model.summary())

                                      OLS Regression Results                                     
=================================================================================================
Dep. Variable:     Feeling of happiness (NV_Q46)_inverse   R-squared:                       0.247
Model:                                               OLS   Adj. R-squared:                  0.246
Method:                                    Least Squares   F-statistic:                     196.7
Date:                                   Wed, 12 Feb 2025   Prob (F-statistic):           1.33e-74
Time:                                           23:27:39   Log-Likelihood:                -848.76
No. Observations:                                   1200   AIC:                             1704.
Df Residuals:                                       1197   BIC:                             1719.
Df Model:                                              2                                         
Covariance Type:                               nonrobust                                         
===============================================================================================================================
                                                                  coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------------------------------------------------------
const                                                           1.9535      0.052     37.509      0.000       1.851       2.056
Satisfaction with financial situation of household (NV_Q50)     0.0813      0.008     10.526      0.000       0.066       0.096
How much freedom of choice and control (Q48)                    0.0708      0.008      9.360      0.000       0.056       0.086
==============================================================================
Omnibus:                       16.545   Durbin-Watson:                   1.777
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               27.717
Skew:                           0.039   Prob(JB):                     9.58e-07
Kurtosis:                       3.740   Cond. No.                         35.6
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

# Predecting y values
DataFrame['y_pred'] = model.predict(X)

# Create grid for regression plane
x1_range = np.linspace(DataFrame['Satisfaction with financial situation of household (NV_Q50)'].min(), 
                    DataFrame['Satisfaction with financial situation of household (NV_Q50)'].max(), 10)
x2_range = np.linspace(DataFrame['How much freedom of choice and control (Q48)'].min(), 
                    DataFrame['How much freedom of choice and control (Q48)'].max(), 10)

x1_grid, x2_grid = np.meshgrid(x1_range, x2_range)
x1_flat = x1_grid.flatten()
x2_flat = x2_grid.flatten()

# Prepare the grid for prediction
grid_data = pd.DataFrame({
    'const': 1,
    'Satisfaction with financial situation of household (NV_Q50)': x1_flat,
    'How much freedom of choice and control': x2_flat
})

# Predict values for the regression plane
y_grid = model.predict(grid_data)
y_grid = y_grid.values.reshape(x1_grid.shape)

# Visualize using Plotly
fig = go.Figure()

# Add actual data points
fig.add_trace(go.Scatter3d(
    x=DataFrame['Satisfaction with financial situation of household (NV_Q50)'], 
    y=DataFrame['How much freedom of choice and control (Q48)'], 
    z=DataFrame['Feeling of happiness (NV_Q46)_inverse'],
    mode='markers',
    marker=dict(size=5, color='blue'),
    name='Actual',
    hovertemplate=(
        'Satisfaction: %{x}<br>'
        'freedom of choice and control: %{y}<br>'
        'Happiness: %{z}<br>'
    )
))

# Add predicted data points
fig.add_trace(go.Scatter3d(
    x=DataFrame['Satisfaction with financial situation of household (NV_Q50)'], 
    y=DataFrame['How much freedom of choice and control (Q48)'], 
    z=DataFrame['y_pred'],
    mode='markers',
    marker=dict(size=5, color='red'),
    name='Predicted',
    hovertemplate=(
        'Satisfaction: %{x}<br>'
        'freedom of choice and control: %{y}<br>'
        'Predicted Happiness: %{z}<br>'
    )
))

# Add regression plane
fig.add_trace(go.Surface(
    x=x1_grid, 
    y=x2_grid, 
    z=y_grid, 
    colorscale='Viridis',
    opacity=0.5,
    name='Regression Plane',
    hovertemplate=(
        'Satisfaction: %{x}<br>'
        'freedom of choice and control: %{y}<br>'
        'Plane Value: %{z}<br>'
    )
))

# Customize layout to show all values on the axis
fig.update_layout(
    scene=dict(
        xaxis=dict(
            title='Satisfaction with financial situation',
            range=[DataFrame['Satisfaction with financial situation of household (NV_Q50)'].min(),
                DataFrame['Satisfaction with financial situation of household (NV_Q50)'].max()],
            tickangle=-30               # Rotate ticks slightly for better readability
        ),
        yaxis=dict(
            title='freedom of choice and control',
            range=[DataFrame['How much freedom of choice and control (Q48)'].min(),
                DataFrame['How much freedom of choice and control (Q48)'].max()],
            tickangle=-30               # Rotate ticks slightly
        ),
        zaxis=dict(
            title='Happiness',
            range=[DataFrame['Feeling of happiness (NV_Q46)_inverse'].min(),
                DataFrame['Feeling of happiness (NV_Q46)_inverse'].max()]
        )
    ),
    title='Multilinear Regression',
    margin=dict(l=50, r=50, b=50, t=50),  # Adjust margins for cleaner layout
    legend=dict(
        orientation="h",  # Horizontal legend layout
        yanchor="bottom",  # Anchor to the bottom of the plot
        y=-0.2,            # Position slightly below the plot
        xanchor="center",  # Center the legend
        x=0.5              # Align horizontally at the center
    )
)

fig.show()

#pio.write_html(fig, file='plot.html', auto_open=True)

# setupping the map
morocco_data = gpd.read_file("ma.json")
morocco_data.drop(columns='source',inplace=True)
# if the file was in goejson fiormat u need to load t as json format 
# morocco_geojson = json.loads(morocco_data.to_json())

morocco_data

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
File ~\AppData\Roaming\Python\Python312\site-packages\IPython\core\formatters.py:343, in BaseFormatter.__call__(self, obj)
    341     pass
    342 else:
--> 343     return printer(obj)
    344 # Finally look for special method names
    345 method = get_real_method(obj, self.print_method)

Cell In[1], line 227, in __DW_OUTPUT_FORMATTER__.<locals>.DataWrangler.formatter(cls, var, **kwargs)
    225 @classmethod
    226 def formatter(cls, var, **kwargs):
--> 227     s = cls(var, **kwargs)
    228     return s._repr_dw_()

Cell In[1], line 221, in __DW_OUTPUT_FORMATTER__.<locals>.DataWrangler.__init__(self, expr_val)
    218 self.id = str(uuid.uuid4())
    219 pandas_df, conversion_method = api["pandas_transport"]["convert_to_pandas"](expr_val)
    220 tmp_vars[self.id] = {
--> 221     "converted": pandas_df.copy(deep=False), # create a shallow copy in case a displayed object is mutated in the same cell
    222     "conversion_method": conversion_method
    223 }

AttributeError: 'NoneType' object has no attribute 'copy'

labeled_df['N_REGION_ISO']

0       MA-09 Souss-Massa
1       MA-09 Souss-Massa
2       MA-09 Souss-Massa
3       MA-09 Souss-Massa
4       MA-09 Souss-Massa
              ...        
1195     MA-02 L'Oriental
1196     MA-02 L'Oriental
1197     MA-02 L'Oriental
1198     MA-02 L'Oriental
1199     MA-02 L'Oriental
Name: N_REGION_ISO, Length: 1200, dtype: object

# functoin to seperate the region id from its name
id_list = []
regions_name = []
y = labeled_df['N_REGION_ISO'].str.split(' ')
for x in y:
    id_list.append(x[0])
    regions_name.append(x[1])

# creating the dataframe the will be ploted 'chromaplot_df'
chromaplot_df = pd.DataFrame({
    'Satisfaction with financial situation of household (NV_Q50)': df['Q50'],
    'Feeling of happiness (NV_Q46)': df['Q46'],
    'Feeling of happiness (NV_Q46)_reversed': DataFrame['Feeling of happiness (NV_Q46)_inverse'],
    'Feeling of happiness (LV_Q46)': labeled_df['Q46'],
    'N_REGION_ISO': labeled_df['N_REGION_ISO'],
    
})
chromaplot_df['ID_list'] = id_list
chromaplot_df['ID_list']= chromaplot_df['ID_list'].str.replace('-', '')

chromaplot_df['regions_name'] = regions_name

chromaplot_df

# calculating  the mean of Feeling of happiness for each region 
aggregated_data_FoH = chromaplot_df.groupby(['ID_list','N_REGION_ISO'])['Feeling of happiness (NV_Q46)_reversed'].mean().reset_index()
aggregated_data_FoH

# calculating  the mean of Satisfaction with financial situation of household for each region 
aggregated_data_SFHS = chromaplot_df.groupby(['ID_list','N_REGION_ISO'])['Satisfaction with financial situation of household (NV_Q50)'].mean().reset_index()
aggregated_data_SFHS

# updating the chromaplot_df 
chromaplot_df = chromaplot_df.merge(aggregated_data_FoH, how='left', on='ID_list')
chromaplot_df = chromaplot_df.merge(aggregated_data_SFHS, how='left', on='ID_list')
chromaplot_df.rename(columns={
    'Feeling of happiness (NV_Q46)_reversed_x': 'Feeling of happiness (NV_Q46)_reversed',
    'Feeling of happiness (NV_Q46)_reversed_y': 'mean_FoH',
    'Satisfaction with financial situation of household (NV_Q50)_x':'Satisfaction with financial situation of household (NV_Q50)',
    'Satisfaction with financial situation of household (NV_Q50)_y':'mean_SFSH'
}, 
inplace=True)
chromaplot_df
# re-ordering the placement of the columns 
reorder=['ID_list','regions_name','N_REGION_ISO','Feeling of happiness (LV_Q46)','Feeling of happiness (NV_Q46)','Feeling of happiness (NV_Q46)_reversed','mean_FoH','Satisfaction with financial situation of household (NV_Q50)','mean_SFSH']
chromaplot_df = chromaplot_df[reorder]
chromaplot_df

# grouping the values by ID_list
chromaplot_df.groupby('ID_list')[['regions_name','N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index()

# creating choropleth plots
fig = px.choropleth_mapbox(
    chromaplot_df,
    geojson=morocco_data,
    locations='ID_list',           # Unique region IDs in your DataFrame
    featureidkey="properties.id",  # Matches IDs in GeoJSON
    color='mean_FoH',  # Column to visualize
    hover_name='N_REGION_ISO',  # Column to display region name
    hover_data={
        'N_REGION_ISO' : True
    },   
    mapbox_style="carto-positron", # Base map style
    zoom=4,                        # Zoom level
    center={"lat": 31.7917, "lon": -7.0926},  # Center on Morocco
    color_continuous_scale="rdbu",  # Color scale for data visualization
    title="Average Feeling of Happiness in Morocco", # Map title
    range_color=[0, 4]
)
fig.update_layout(
    margin={"r":0,"t":30,"l":0,"b":0}  # Removes excess margins
)
fig.show()

# pio.write_html(fig, file='plot.html', auto_open=True)  # Save and open the plot in a browser

# showing the rating of the regions based on the Mean Feeling of happiness (reversed) for every region for every region
chromaplot_df.groupby('ID_list')[['N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index().sort_values(by='mean_FoH', ascending=False)

#creating choropleth plots
fig = px.choropleth_mapbox(
    chromaplot_df,
    geojson=morocco_data,
    locations='ID_list',           # Unique region IDs in your DataFrame
    featureidkey="properties.id",  # Matches IDs in GeoJSON
    color='mean_SFSH',  # Column to visualize
    hover_name='N_REGION_ISO',  # Column to display region name
    hover_data={
        'N_REGION_ISO' : True,
        'mean_FoH': True
    },   
    mapbox_style="carto-positron", # Base map style
    zoom=4,                        # Zoom level
    center={"lat": 31.7917, "lon": -7.0926},  # Center on Morocco
    color_continuous_scale="deep",  # Color scale for data visualization
    title="Average Satisfaction with financial situation of household in Morocco",# Map title
    range_color=[0,10]
    )
fig.update_layout(
    margin={"r":0,"t":30,"l":0,"b":0}  # Removes excess margins
)
fig.show()

#pio.write_html(fig, file='plot.html', auto_open=True)  # Save and open the plot in a browser

# showing the rating of the regions based on the Mean of Satisfaction with financial situation of household for every region 
chromaplot_df.groupby('ID_list')[['N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index().sort_values(by='mean_SFSH', ascending=False)

	Column	Label	Value
0	version	Version of Data File	{}
1	doi	Digital Object Identifier	{}
2	A_YEAR	Year of survey	{2017.0: '2017', 2018.0: '2018', 2019.0: '2019...
3	B_COUNTRY	ISO 3166-1 numeric country code	{504.0: 'Morocco'}
4	B_COUNTRY_ALPHA	ISO 3166-1 alpha-3 country code	{}
...	...	...	...
425	Y001_1	Materialist/postmaterialist 12-item index: Com...	{-2.0: '-2', 0.0: '0', 1.0: '1'}
426	Y001_2	Materialist/postmaterialist 12-item index: Com...	{-2.0: '-2', 0.0: '0', 1.0: '1'}
427	Y001_3	Materialist/postmaterialist 12-item index: Com...	{-2.0: '-2', 0.0: '0', 1.0: '1'}
428	Y001_4	Materialist/postmaterialist 12-item index: Com...	{-2.0: '-2', 0.0: '0', 1.0: '1'}
429	Y001_5	Materialist/postmaterialist 12-item index: Com...	{-2.0: '-2', 0.0: '0', 1.0: '1'}

	version	doi	A_YEAR	B_COUNTRY	B_COUNTRY_ALPHA	C_COW_NUM	C_COW_ALPHA	D_INTERVIEW	J_INTDATE	FW_START	FW_END	K_TIME_START	K_TIME_END	K_DURATION	Q_MODE	N_REGION_ISO	N_REGION_WVS	N_TOWN	G_TOWNSIZE	G_TOWNSIZE2	H_SETTLEMENT	H_URBRURAL	I_PSU	O1_LONGITUDE	O2_LATITUDE	S_INTLANGUAGE	LNGE_ISO	E_RESPINT	F_INTPRIVACY	W_WEIGHT	S018	PWGHT	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20	Q21	Q22	Q23	Q24	Q25	Q26	Q27	Q28	Q29	Q30	Q31	Q32	Q33	Q33_3	Q34	Q34_3	Q35	Q35_3	Q37	Q38	Q39	Q40	Q41	Q42	Q43	Q44	Q45	Q46	Q47	Q48	Q49	Q50	Q51	Q52	Q53	Q54	Q55	Q56	Q57	Q58	Q59	Q60	Q61	Q62	Q63	Q64	Q65	Q66	Q67	Q68	Q69	Q70	Q71	Q72	Q73	Q74	Q75	Q76	Q77	Q78	Q79	Q80	Q81	Q82	Q82_ARABLEAGUE	Q82_GULFCOOP	Q82_ISLCOOP	Q83	Q84	Q85	Q86	Q87	Q88	Q89	Q90	Q91	Q92	Q93	Q94	Q95	Q96	Q97	Q98	Q99	Q100	Q101	Q102	Q103	Q104	Q105	Q106	Q107	Q108	Q109	Q110	Q111	Q112	Q113	Q114	Q115	Q116	Q117	Q118	Q119	Q120	Q121	Q122	Q123	Q124	Q125	Q126	Q127	Q128	Q129	Q130	Q131	Q132	Q133	Q134	Q135	Q136	Q137	Q138	Q139	Q140	Q141	Q142	Q143	Q144	Q145	Q146	Q147	Q148	Q149	Q150	Q151	Q152	Q153	Q154	Q155	Q156	Q157	Q158	Q159	Q160	Q161	Q162	Q163	Q164	Q165	Q166	Q167	Q168	Q169	Q170	Q171	Q172	Q173	Q174	Q175	Q176	Q177	Q178	Q179	Q180	Q181	Q182	Q183	Q184	Q185	Q186	Q187	Q188	Q189	Q190	Q191	Q192	Q193	Q194	Q195	Q196	Q197	Q198	Q199	Q200	Q201	Q202	Q203	Q204	Q205	Q206	Q207	Q208	Q209	Q210	Q211	Q212	Q213	Q214	Q215	Q216	Q217	Q218	Q219	Q220	Q221	Q222	Q223	Q223_ABREV	Q223_LOCAL	Q224	Q225	Q226	Q227	Q228	Q229	Q230	Q231	Q232	Q233	Q234	Q234A	Q235	Q236	Q237	Q238	Q239	Q240	Q241	Q242	Q243	Q244	Q245	Q246	Q247	Q248	Q249	Q250	Q251	Q252	Q253	Q254	Q255	Q256	Q257	Q258	Q259	Q260	Q261	Q262	X003R	X003R2	Q263	Q264	Q265	Q266	Q267	Q268	Q269	Q270	Q271	Q272	Q273	Q274	Q275	Q275R	Q276	Q276R	Q277	Q277R	Q278	Q278R	Q279	Q280	Q281	Q282	Q283	Q284	Q285	Q286	Q287	Q288	Q288R	Q289	Q289CS9	Q290	Q291G1	Q291G2	Q291G3	Q291G4	Q291G5	Q291G6	Q291P1	Q291P2	Q291P3	Q291P4	Q291P5	Q291P6	Q291UN1	Q291UN2	Q291UN3	Q291UN4	Q291UN5	Q291UN6	Q292A	Q292B	Q292C	Q292D	Q292E	Q292F	Q292G	Q292H	Q292I	Q292J	Q292K	Q292L	Q292M	Q292N	Q292O	Q293	Q294A	Q294B	Y001	Y002	Y003	SACSECVAL	SACSECVALB	RESEMAVAL	RESEMAVALB	I_AUTHORITY	I_NATIONALISM	I_DEVOUT	DEFIANCE	I_RELIGIMP	I_RELIGBEL	I_RELIGPRAC	DISBELIEF	I_NORM1	I_NORM2	I_NORM3	RELATIVISM	I_TRUSTARMY	I_TRUSTPOLICE	I_TRUSTCOURTS	SCEPTICISM	I_INDEP	I_IMAGIN	I_NONOBED	AUTONOMY	I_WOMJOB	I_WOMPOL	I_WOMEDU	EQUALITY	I_HOMOLIB	I_ABORTLIB	I_DIVORLIB	CHOICE	I_VOICE1	I_VOICE2	I_VOI2_00	VOICE	SECVALWGT	WEIGHT1A	WEIGHT1B	WEIGHT2A	WEIGHT2B	WEIGHT3A	WEIGHT3B	WEIGHT4A	WEIGHT4B	RESEMAVALBWGT	RESEMAVALWGT	SECVALBWGT	Y001_1	Y001_2	Y001_3	Y001_4	Y001_5
0	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504070001.0	20211112.0	202111.0	202112.0	10.05	10.43	38.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were other people around who could follo...	No weighting	0.833333	31083.33333	Very important	Rather important	Very important	Very important	Very important	Very important	Important	Not mentioned	Important	Important	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Important	Not mentioned	Important	Mentioned	Not mentioned	Not mentioned	Mentioned	Mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Agree strongly	Agree	Agree strongly	Disagree	Agree strongly	Agree strongly	Agree	Agree	Agree strongly	Agree	Strongly agree	Agree	Agree	Agree strongly	Strongly agree	Strongly agree	Neither agree nor disagree	Our society must be gradually improved by reforms	Don't mind	Good thing	Bad thing	Quite happy	Fair	5	8	6	Never	Rarely	Never	Never	Never	Or about the same	Need to be very careful	Trust completely	Do not trust very much	Trust somewhat	Do not trust at all	Do not trust at all	Trust somewhat	Quite a lot	Not very much	Not very much	Quite a lot	Not very much	Not very much	A great deal	None at all	None at all	None at all	Not very much	Quite a lot	A great deal	A great deal	Not very much	A great deal	Quite a lot	Not very much	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Not very much	Quite a lot	Not very much	None at all	None at all	Quite a lot	5	India	Geneva	Human rights	Inactive member	Don't belong	Don't belong	Not a member	Not a member	Don't belong	Not a member	Not a member	Don't belong	Don't belong	Don't belong	Don't belong	6	4	5	5	5	Economy growth and creating jobs	10 There is abundant corruption in my country	Most of them	Most of them	Few of them	Few of them	Most of them	Frequently	Disagree	8	Neither good, nor bad	Agree	Agree	Hard to say	Agree	Hard to say	Agree	Hard to say	Disagree	Place strict limits on the number of foreigner...	Quite secure	Not frequently	Not frequently	Quite frequently	Quite frequently	Not frequently	Not frequently	Not frequently	Yes	No	Yes	A great deal	A great deal	No	No	Very much	Very much	A great deal	Equality	Security	No	A high level of economic growth	Seeing that people have more say about how ar...	Maintaining order in the nation	Protecting freedom of speech	Progress toward a less impersonal and more hum...	The fight against crime	6	8	8	5	6	8	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	More than once a week	Several times a day	Not a religious person	Do good to other people	Make sense of life after death	4	5	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	7	2	Never justifiable	6	2	8	Never justifiable	Never justifiable	Never justifiable	3	Always justifiable	Definitely should have the right	Definitely should not have the right	Definitely should not have the right	Somewhat interested	Never	Never	Daily	Weekly	Weekly	Less than monthly	Weekly	Weekly	Never	Have done	Have done	Would never do	Would never do	Might do	Would never do	Might do	Would never do	Might do	Would never do	Would never do	Would never do	Usually	Usually	MAR: Authenticity and Modernity Party	MAR: PAM	MAR: Parti de l'Authenticite et de la Modernit...	Very often	Not often	Fairly often	Very often	Very often	Very often	Very often	Not at all often	Not at all often	Not often	Rather important	Not at all	Very bad	Very bad	Very bad	Fairly good	Very good	8	An essential characteristic of democracy	7	6	9	4	8	9	5	An essential characteristic of democracy	Absolutely important	3	Not satisfied at all	Not much respect	Not very proud	Not close at all	Not close at all	Not close at all	Not very close	Not close at all	Female	1969.0	52.0	45-54	50 and more years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	8.0	No	Berber; Amazigh;Tamaziɣt	Married	4 children	Primary education (ISCED 1)	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Full time (30 hours a week or more)	Homemaker not otherwise employed	Service (for example: restaurant owner, police...	Farm worker (for example: farm labourer, tract...	Farm worker (for example: farm labourer, tract...	Private business or industry	Yes	Spent some savings	Upper middle class	Sixth step	Medium	Muslim	Islam; nfd	MA: Arabe	Disagree strongly	Agree	Disagree strongly	Disagree	Disagree strongly	Disagree strongly	Disagree strongly	Agree	Disagree strongly	Disagree	Disagree strongly	Disagree	Neither agree nor disagree	Disagree	Disagree	Agree	Disagree strongly	Neither agree nor disagree	Agree strongly	Agree	Neither agree nor disagree	Disagree	Disagree	Agree	Disagree	Disagree	Disagree strongly	Disagree strongly	Disagree strongly	Agree strongly	Agree	Disagree strongly	Neither agree nor disagree	1	1	1	3	Mixed	Obedience/Religious Faith	0.331667	0.443333	0.235139	0.262778	High	High	Very low	0.553333	Very low	Not religious or atheist	0.0	0.333333	Conformist	Conformist	Conformist	0.0	Low	Low	Very high	0.44	No	No	No	0.0	Medium-Low	Very low	High	0.303333	0.0	0.0	0.666667	0.222222	High	Medio	0.415	0.415	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	1	0	1	1	0
1	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504070002.0	20211112.0	202111.0	202112.0	10.46	11.19	33.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Very important	Rather important	Not very important	Not at all important	Very important	Very important	Important	Not mentioned	Important	Not mentioned	Important	Not mentioned	Important	Not mentioned	Important	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Mentioned	Mentioned	Mentioned	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Agree strongly	Agree strongly	Agree strongly	Disagree	Agree strongly	Agree strongly	Agree strongly	Agree	Agree strongly	Agree	Disagree strongly	Disagree	Agree strongly	Agree strongly	Strongly agree	Strongly agree	Agree strongly	Our present society must be valiantly defended...	Bad thing	Good thing	Good thing	Not at all happy	Poor	5	4	2	Never	Often	Often	Sometimes	Never	Or about the same	Need to be very careful	Trust completely	Do not trust very much	Do not trust at all	Do not trust at all	Do not trust at all	Do not trust very much	A great deal	A great deal	None at all	None at all	Not very much	A great deal	Not very much	None at all	None at all	None at all	Not very much	Not very much	None at all	None at all	None at all	Quite a lot	Quite a lot	A great deal	Not very much	Not very much	Not very much	Quite a lot	None at all	None at all	None at all	None at all	None at all	None at all	None at all	5	India	London	Human rights	Active member	Don't belong	Active member	Not a member	Not a member	Don't belong	Not a member	Not a member	Don't belong	Don't belong	Don't belong	Don't belong	There should be greater incentives for individ...	Government ownership of business should be inc...	9	6	6	Protecting environment	10 There is abundant corruption in my country	Most of them	All of them	Most of them	None of them	All of them	Always	Hard to say	Very high risk	Rather bad	Agree	Agree	Disagree	Disagree	Agree	Agree	Agree	Agree	Prohibit people coming here from other countries	Quite secure	Very Frequently	Very Frequently	Very Frequently	Very Frequently	Quite frequently	Very Frequently	Very Frequently	Yes	Yes	No	Very much	Very much	Yes	Yes	Very much	Very much	Very much	Freedom	Security	Yes	A high level of economic growth	Trying to make our cities and countryside more...	Maintaining order in the nation	Fighting rising prices	Progress toward a less impersonal and more hum...	Progress toward a society in which Ideas count...	Completely agree	Completely agree	Completely agree	Completely agree	8	A lot worse off	Not at all important	Yes	No	No	No	Strongly disagree	Strongly disagree	Once a year	Only on special holy days	A religious person	Do good to other people	Make sense of life in this world	5	5	5	5	5	5	5	5	5	8	5	5	5	5	9	5	5	5	5	6	Definitely should have the right	Definitely should not have the right	Definitely should not have the right	Somewhat interested	Occasionally	Less than monthly	Less than monthly	Never	Daily	Never	Daily	Daily	Less than monthly	Might do	Have done	Might do	Might do	Have done	Might do	Might do	Would never do	Might do	Might do	Would never do	Would never do	Never	Never	MAR: Justice and Development Party	MAR: PJD	MAR: Parti de la Justice et du Developpement -...	Not at all often	Not often	Not at all often	Not at all often	Not at all often	Not at all often	Very often	Very often	Not often	Fairly often	Very important	Not at all	Very good	Very good	Very bad	Very good	Very good	Left	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	5	8	6	7	Not much respect	Very proud	Very close	Very close	Close	Not very close	Not close at all	Female	1998.0	23.0	16-24	16-29 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	6	Yes, own parent(s)	Berber; Amazigh;Tamaziɣt	Single	No children	Bachelor or equivalent (ISCED 6)	Higher	Upper secondary education (ISCED 3)	Middle	Early childhood education (ISCED 0) / no educa...	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Full time (30 hours a week or more)	NaN	Sales (for example: sales manager, shop owner,...	NaN	Skilled worker (for example: foreman, motor me...	Private non-profit organization	No	Just get by	Working class	Seventh step	Medium	Muslim	Islam; nfd	MA: Arabe	Disagree strongly	Agree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Agree strongly	Agree strongly	Agree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Agree strongly	Disagree strongly	Neither agree nor disagree	Neither agree nor disagree	Neither agree nor disagree	Agree	Agree strongly	Agree strongly	Disagree strongly	Disagree strongly	Agree strongly	Agree	Disagree	Disagree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Agree strongly	Disagree strongly	Disagree strongly	No trust at all	NaN	NaN	2	Materialist	-1	0.360556	0.111111	0.360556	0.387778	Low	Very low	Very low	0.0	Very low	Religious	0.666667	0.222222	Inconformist	Inconformist	Inconformist	1.0	Very high	Very high	Low	0.22	No	Yes	Yes	0.666667	Low	Very low	High	0.22	0.444444	0.444444	0.777778	0.555556	Very high	Bajo	0.0	0.0	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	0	1	1
2	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504070003.0	20211112.0	202111.0	202112.0	11.28	12.09	41.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were other people around who could follo...	No weighting	0.833333	31083.33333	Very important	Rather important	Very important	Not at all important	Very important	Very important	Important	Not mentioned	Important	Important	Not mentioned	Important	Not mentioned	Important	Important	Not mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Agree strongly	Agree strongly	Agree strongly	Disagree	Agree	Disagree	Neither agree nor disagree	Neither agree nor disagree	Agree strongly	Agree	Strongly agree	Agree	Agree strongly	Agree strongly	Strongly agree	Strongly agree	Agree strongly	The entire way our society is organized must b...	Bad thing	Good thing	Good thing	Not very happy	Poor	2	2	3	Rarely	Rarely	Sometimes	Often	Often	Worse off	Need to be very careful	Trust completely	Do not trust at all	Do not trust at all	Do not trust at all	Do not trust at all	Do not trust at all	Quite a lot	A great deal	Quite a lot	Not very much	None at all	A great deal	A great deal	Not very much	Not very much	None at all	A great deal	Quite a lot	None at all	A great deal	A great deal	A great deal	Not very much	Not very much	None at all	None at all	None at all	None at all	Not very much	None at all	None at all	None at all	A great deal	Quite a lot	A great deal	9	India	Geneva	Human rights	Don't belong	Active member	Don't belong	Not a member	Not a member	Don't belong	Not a member	Not a member	Don't belong	Don't belong	Don't belong	Don't belong	Incomes should be made more equal	Private ownership of business should be increased	The government should take more responsibility...	Competition is good	In the long run, hard work usually brings a be...	Protecting environment	10 There is abundant corruption in my country	Few of them	Most of them	Few of them	Few of them	Most of them	Always	Strongly disagree	Very high risk	Quite good	Disagree	Agree	Agree	Agree	Disagree	Agree	Agree	Disagree	Let people come as long as there are jobs avai...	Quite secure	Not frequently	Not at all frequently	Not at all frequently	Quite frequently	Not frequently	Not frequently	Very Frequently	Yes	Yes	No	Not at all	A great deal	Yes	Yes	Very much	Very much	Very much	Equality	Freedom	Yes	A high level of economic growth	Making sure this country has strong defence fo...	Maintaining order in the nation	Protecting freedom of speech	Progress toward a less impersonal and more hum...	Progress toward a society in which Ideas count...	8	8	7	5	2	7	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	Once a week	Several times a day	Not a religious person	Follow religious norms and ceremonies	Make sense of life after death	4	7	2	2	2	Never justifiable	Never justifiable	Never justifiable	4	7	2	Never justifiable	Never justifiable	5	3	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Definitely should have the right	Probably should not have the right	Probably should not have the right	Not at all interested	Never	Never	Never	Never	Never	Never	Never	Weekly	Weekly	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Might do	Would never do	Would never do	Would never do	Never	Never	None	4.0	4.0	Very often	Not at all often	Fairly often	Not often	Not often	Very often	Fairly often	Not often	Not often	Very often	Not very important	Very little	Very good	Very good	Fairly bad	Fairly good	Fairly good	8	An essential characteristic of democracy	7	An essential characteristic of democracy	An essential characteristic of democracy	5	An essential characteristic of democracy	6	8	An essential characteristic of democracy	Absolutely important	6	6	Fairly much respect	Quite proud	Very close	Close	Not close at all	Not close at all	Not close at all	Male	1986.0	35.0	35-44	30-49 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	4	Yes, own parent(s)	Arabic	Divorced	1 child	Bachelor or equivalent (ISCED 6)	Higher	Post-secondary non-tertiary education (ISCED 4)	Middle	Bachelor or equivalent (ISCED 6)	Higher	Bachelor or equivalent (ISCED 6)	Higher	Unemployed	NaN	Farm worker (for example: farm labourer, tract...	Farm worker (for example: farm labourer, tract...	Farm worker (for example: farm labourer, tract...	Private non-profit organization	Yes	Just get by	Upper middle class	Seventh step	Medium	Muslim	Islam; nfd	MA: Arabe	Disagree strongly	Disagree	Agree strongly	Disagree	Disagree	Agree strongly	Disagree strongly	Disagree strongly	Agree	Disagree	Disagree	Agree	Agree	Neither agree nor disagree	Agree	Neither agree nor disagree	Agree	Agree strongly	Agree strongly	Agree strongly	Neither agree nor disagree	Disagree	Agree	Agree strongly	Disagree strongly	Neither agree nor disagree	Agree strongly	Agree	Disagree strongly	Agree	Agree	Agree	Disagree	7	NaN	NaN	3	Mixed	0	0.291389	0.249444	0.304583	0.36	Low	Low	Very low	0.11	Very low	Not religious or atheist	0.166667	0.388889	Inconformist	Inconformist	Conformist	0.666667	Very high	Very high	Very high	0.0	No	No	Yes	0.333333	Medium	Very low	High	0.386667	0.0	0.333333	0.666667	0.333333	High	Bajo	0.165	0.165	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	1	1	1
3	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504070004.0	20211112.0	202111.0	202112.0	12.08	12.42	34.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Very important	Very important	Not at all important	Rather important	Very important	Very important	Important	Not mentioned	Important	Not mentioned	Not mentioned	Important	Not mentioned	Not mentioned	Not mentioned	Important	Important	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Agree strongly	Agree strongly	Agree strongly	Disagree	Disagree	Agree strongly	Agree strongly	Agree	Agree strongly	Agree	Strongly agree	Agree	Agree strongly	Agree strongly	Strongly agree	Strongly agree	Agree strongly	Our present society must be valiantly defended...	Bad thing	Good thing	Good thing	Quite happy	Very good	8	Completely dissatisfied	6	Never	Rarely	Never	Never	Never	Or about the same	Need to be very careful	Trust completely	Do not trust very much	Do not trust very much	Do not trust at all	Do not trust very much	Do not trust very much	Not very much	A great deal	None at all	None at all	None at all	A great deal	A great deal	Not very much	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	Not very much	Not very much	Not very much	Not very much	Not very much	None at all	None at all	Not very much	None at all	None at all	None at all	Being democratic	India	Geneva	Human rights	Don't belong	Don't belong	Don't belong	Not a member	Not a member	Don't belong	Not a member	Not a member	Don't belong	Don't belong	Don't belong	Don't belong	There should be greater incentives for individ...	Government ownership of business should be inc...	People should take more responsibility to prov...	Competition is harmful	Hard work doesn't generally bring success - it...	Economy growth and creating jobs	7	Few of them	Most of them	Few of them	Few of them	Most of them	Frequently	Strongly agree	Very high risk	Quite good	Agree	Disagree	Hard to say	Hard to say	Disagree	Disagree	Agree	Disagree	Place strict limits on the number of foreigner...	Very Secure	Not frequently	Quite frequently	Quite frequently	Not at all frequently	Quite frequently	Not frequently	Very Frequently	Yes	Yes	Yes	Very much	Very much	No	No	Very much	Very much	Very much	Equality	Freedom	Yes	A high level of economic growth	Trying to make our cities and countryside more...	Maintaining order in the nation	Fighting rising prices	A stable economy	Progress toward a society in which Ideas count...	Completely agree	Completely agree	Completely agree	8	Completely disagree	A lot better off	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	More than once a week	Several times a day	A religious person	Do good to other people	Make sense of life in this world	Completely agree	5	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Definitely should have the right	Definitely should not have the right	Definitely should not have the right	Somewhat interested	Occasionally	Less than monthly	Daily	Less than monthly	Daily	Daily	Daily	Daily	Daily	Would never do	Have done	Would never do	Would never do	Have done	Might do	Have done	Have done	Have done	Have done	Have done	Have done	Usually	Usually	None	4.0	4.0	Not often	Not often	Very often	Very often	Not often	Fairly often	Fairly often	Fairly often	Very often	Fairly often	Very important	Some	Very good	Very bad	Very bad	Very bad	Bad	5	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	Not an essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	Not an essential characteristic of democracy	Not an essential characteristic of democracy	Absolutely important	6	6	A great deal of respect	Very proud	Close	Not close at all	Not close at all	Not close at all	Not close at all	Male	1993.0	28.0	25-34	16-29 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	4	Yes, own parent(s)	Arabic	Married	2 children	Primary education (ISCED 1)	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Lower secondary education (ISCED 2)	Lower	Lower secondary education (ISCED 2)	Lower	Unemployed	Full time (30 hours a week or more)	Clerical (for example: secretary, clerk, offic...	Never had a job	Farm worker (for example: farm labourer, tract...	Private business or industry	No	Just get by	Lower middle class	Sixth step	Medium	Muslim	Islam; nfd	MA: Arabe	Agree	Agree strongly	Agree strongly	Agree	Disagree strongly	Neither agree nor disagree	Disagree	Agree strongly	Agree strongly	Agree	Disagree strongly	Disagree	Agree strongly	Agree strongly	Agree strongly	Neither agree nor disagree	Disagree	Disagree	Agree	Agree	Agree	Agree	Agree	Disagree	Agree	Agree	Agree	Agree	Agree	Agree	Agree	Agree	Agree	5	NaN	NaN	1	Materialist	-1	0.0	0.0	0.055	0.11	Low	Very low	Very low	0.0	Very low	Religious	0.0	0.0	Conformist	Conformist	Conformist	0.0	Very high	Very high	Very high	0.0	No	No	No	0.0	Low	Very low	High	0.22	0.0	0.0	0.0	0.0	Very high	Bajo	0.0	0.0	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	0	0	1
4	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504070005.0	20211112.0	202111.0	202112.0	13.18	13.55	37.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Very important	Rather important	Not very important	Not very important	Very important	Very important	Important	Important	Not mentioned	Important	Not mentioned	Not mentioned	Not mentioned	Important	Not mentioned	Important	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Agree strongly	Agree strongly	Disagree	Disagree	Agree	Agree strongly	Agree strongly	Agree	Agree strongly	Agree	Neither agree nor disagree	Neither agree nor disagree	Neither agree nor disagree	Agree	Disagree	Disagree	Neither agree nor disagree	The entire way our society is organized must b...	Bad thing	Don't mind	Don't mind	Not very happy	Good	7	7	4	Sometimes	Rarely	Sometimes	Never	Never	Or about the same	Need to be very careful	Trust completely	Do not trust very much	Trust somewhat	Do not trust very much	Do not trust very much	Do not trust very much	Quite a lot	Quite a lot	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	None at all	Not very much	None at all	None at all	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	5	China	Washington DC	Destruction of historic monuments	Inactive member	Active member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Don't belong	6	5	3	Competition is good	5	Protecting environment	8	Most of them	Most of them	Most of them	Most of them	Most of them	Frequently	Strongly disagree	Very high risk	Neither good, nor bad	Agree	Hard to say	Hard to say	Hard to say	Disagree	Agree	Disagree	Hard to say	Let people come as long as there are jobs avai...	Very Secure	Quite frequently	Quite frequently	Not at all frequently	Quite frequently	Quite frequently	Quite frequently	Very Frequently	Yes	No	No	A great deal	Very much	No	No	Very much	Very much	Very much	Equality	Security	No	A high level of economic growth	Making sure this country has strong defence fo...	Maintaining order in the nation	Fighting rising prices	A stable economy	Progress toward a less impersonal and more hum...	8	Completely agree	7	6	Completely disagree	8	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	More than once a week	Several times a day	Not a religious person	Follow religious norms and ceremonies	Make sense of life after death	7	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	3	Never justifiable	4	Never justifiable	8	2	Never justifiable	4	5	Always justifiable	Definitely should have the right	Definitely should not have the right	Definitely should not have the right	Not at all interested	Never	Never	Never	Never	Never	Never	Daily	Daily	Monthly	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Might do	Would never do	Would never do	Would never do	Never	Never	None	4.0	4.0	Very often	Fairly often	Not at all often	Fairly often	Fairly often	Very often	Fairly often	Not often	Fairly often	Very often	Not very important	Very little	Very bad	Fairly bad	Very bad	Very good	Very good	7	Not an essential characteristic of democracy	3	5	9	6	An essential characteristic of democracy	An essential characteristic of democracy	3	9	8	5	3	Not much respect	Not very proud	Close	Close	Not very close	Not very close	Not close at all	Male	1999.0	22.0	16-24	16-29 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	5	No	Arabic	Single	No children	Primary education (ISCED 1)	Lower	NaN	NaN	Upper secondary education (ISCED 3)	Middle	Lower secondary education (ISCED 2)	Lower	Full time (30 hours a week or more)	NaN	Service (for example: restaurant owner, police...	NaN	Farm worker (for example: farm labourer, tract...	Private non-profit organization	No	Spent some savings	Working class	Seventh step	Medium	Muslim	Islam; nfd	MA: Arabe	Agree	Disagree	Agree strongly	Agree strongly	Disagree	Disagree strongly	Disagree	Disagree	Agree	Agree	Disagree	Agree	Disagree	Disagree	Agree strongly	Disagree	Disagree	Disagree	Agree strongly	Agree strongly	Disagree	Disagree	Agree	Agree strongly	Disagree strongly	Disagree strongly	Agree strongly	Agree strongly	Disagree strongly	Agree strongly	Agree strongly	Agree	Agree strongly	2	1	1	1	Materialist	Determination, perseverance/Independence	0.3175	0.36	0.276667	0.22	Medium	High	Very low	0.386667	Very low	Not religious or atheist	0.0	0.333333	Conformist	Conformist	Conformist	0.0	High	Low	Low	0.55	Yes	No	Yes	0.666667	Low	High	High	0.44	0.0	0.0	0.0	0.0	Very high	Bajo	0.0	0.0	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1195	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504071196.0	20211213.0	202111.0	202112.0	14.12	15.11	59.0	Paper-and-Pencil Interviewing (PAPI)	MA-02 L'Oriental	MA: MA-02 L'Oriental	MA: Zaio	20,000-50,000	20000-100000	District center	Urban	81.0	-2.73	34.94	Arabic	ar	Respondent was very interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Very important	Very important	Very important	Very important	Very important	Very important	Important	Important	Important	Important	Not mentioned	Not mentioned	Not mentioned	Important	Not mentioned	Not mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Agree strongly	Agree strongly	Agree	Disagree	Agree	Agree	Agree strongly	Agree	Agree strongly	Agree	Disagree	Disagree	Agree	Agree strongly	Strongly agree	Strongly agree	Agree	Our society must be gradually improved by reforms	Bad thing	Good thing	Good thing	Very happy	Very good	A great deal	6	6	Never	Never	Rarely	Rarely	Never	Better off	Most people can be trusted	Trust completely	Trust somewhat	Trust completely	Trust somewhat	Trust completely	Trust completely	Quite a lot	Quite a lot	Not very much	Not very much	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Not very much	Not very much	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Not very much	Not very much	Being democratic	India	Washington DC	Human rights	Inactive member	Active member	Inactive member	Inactive member	Active member	Don't belong	Inactive member	Inactive member	Don't belong	Don't belong	Don't belong	Don't belong	There should be greater incentives for individ...	5	People should take more responsibility to prov...	5	5	Economy growth and creating jobs	5	Few of them	Few of them	Few of them	Few of them	Few of them	Frequently	Disagree	4	Quite good	Disagree	Agree	Disagree	Disagree	Disagree	Agree	Disagree	Disagree	Let anyone come who wants to	Quite secure	Not at all frequently	Not at all frequently	Not at all frequently	Not at all frequently	Not at all frequently	Not frequently	Not frequently	Yes	No	No	Very much	Very much	No	No	Very much	Very much	Very much	Freedom	Security	Yes	Seeing that people have more say about how ar...	Making sure this country has strong defence fo...	Giving people more say in important government...	Protecting freedom of speech	Progress toward a less impersonal and more hum...	Progress toward a society in which Ideas count...	9	9	9	9	4	8	Very important	Yes	Yes	Yes	Yes	Disagree	Agree	Never, practically never	Several times each week	Not a religious person	Do good to other people	Make sense of life in this world	4	5	5	5	5	5	5	5	6	6	6	5	5	5	5	4	4	6	5	6	Probably should have the right	Probably should have the right	Probably should have the right	Very interested	Frequently	Never	Weekly	Never	Daily	Monthly	Weekly	Daily	Daily	Have done	Have done	Have done	Have done	Have done	Might do	Might do	Have done	Have done	Have done	Might do	Might do	Always	Always	MAR: National Rally of Independents	MAR: RNI	MAR: Rassemblement National des Independents -...	Very often	Not at all often	Fairly often	Fairly often	Fairly often	Very often	Fairly often	Not often	Very often	Very often	Very important	Some	Fairly Bad	Fairly bad	Fairly bad	Very good	Bad	Right	7	5	7	6	6	7	6	7	6	Absolutely important	Completely democratic	Completely satisfied	Fairly much respect	Quite proud	Close	Close	Close	Close	Close	Male	1980.0	41.0	35-44	30-49 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	5	No	Arabic	Single	No children	Post-secondary non-tertiary education (ISCED 4)	Middle	NaN	NaN	Primary education (ISCED 1)	Lower	Primary education (ISCED 1)	Lower	Full time (30 hours a week or more)	NaN	Professional and technical (for example: docto...	NaN	Skilled worker (for example: foreman, motor me...	Private business or industry	Yes	Just get by	Lower middle class	Sixth step	Medium	Muslim	Islam; nfd	MA: Arabe	Agree	Disagree	Disagree	Agree strongly	Disagree	Agree	Agree	Disagree strongly	Agree	Agree	Disagree	Agree	Agree	Disagree	Disagree	Agree	Agree	Agree	Disagree	Disagree	Agree	Agree	Disagree	Disagree	Agree	Agree	Disagree	Disagree	Agree	Disagree	Disagree	Agree	Agree	5	4	4	Postmaterialist	Postmaterialist	Determination, perseverance/Independence	0.526667	0.388333	0.628796	0.424259	Low	Low	Very low	0.11	Very low	Not religious or atheist	1.0	0.666667	Inconformist	Inconformist	Inconformist	1.0	High	High	High	0.33	Yes	No	Yes	0.666667	Low	Low	High	0.33	0.444444	0.555556	0.555556	0.518519	Very low	Alto	1.0	1.0	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	1	1	1	1	1
1196	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504071197.0	20211213.0	202111.0	202112.0	15.3	16.25	55.0	Paper-and-Pencil Interviewing (PAPI)	MA-02 L'Oriental	MA: MA-02 L'Oriental	MA: Zaio	20,000-50,000	20000-100000	District center	Urban	81.0	-2.73	34.94	Arabic	ar	Respondent was somewhat interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Rather important	Rather important	Rather important	Not at all important	Rather important	Rather important	Not mentioned	Important	Important	Not mentioned	Not mentioned	Not mentioned	Important	Important	Not mentioned	Not mentioned	Important	Mentioned	Not mentioned	Not mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Agree	Agree	Disagree	Disagree	Disagree	Disagree	Neither agree nor disagree	Neither agree nor disagree	Agree strongly	Agree	Disagree	Disagree	Agree	Agree strongly	Strongly agree	Agree	Disagree	The entire way our society is organized must b...	Bad thing	Good thing	Bad thing	Quite happy	Fair	7	6	6	Rarely	Rarely	Rarely	Sometimes	Rarely	Or about the same	Need to be very careful	Do not trust at all	Do not trust at all	Do not trust very much	Do not trust very much	Do not trust very much	Do not trust very much	Not very much	Not very much	Not very much	Not very much	None at all	None at all	None at all	None at all	None at all	None at all	Not very much	Not very much	None at all	Not very much	Not very much	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	5	France	Washington DC	Human rights	Inactive member	Active member	Active member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Don't belong	There should be greater incentives for individ...	Government ownership of business should be inc...	5	5	8	Economy growth and creating jobs	10 There is abundant corruption in my country	All of them	All of them	All of them	All of them	All of them	Always	Agree	3	Neither good, nor bad	Agree	Disagree	Agree	Disagree	Agree	Agree	Agree	Agree	Place strict limits on the number of foreigner...	Not very secure	Quite frequently	Quite frequently	Not frequently	Not frequently	Not frequently	Quite frequently	Quite frequently	Yes	Yes	No	A great deal	A great deal	No	No	A good deal	A great deal	A great deal	Freedom	Security	Yes	A high level of economic growth	Seeing that people have more say about how ar...	Giving people more say in important government...	Fighting rising prices	Progress toward a society in which Ideas count...	The fight against crime	Completely agree	Completely agree	Completely agree	Completely agree	3	A lot better off	6	Yes	Yes	Yes	Yes	Disagree	Agree	Never, practically never	Less often	Not a religious person	Follow religious norms and ceremonies	Make sense of life after death	5	8	4	4	4	4	4	8	8	6	6	6	4	6	6	4	4	6	4	6	Definitely should not have the right	Definitely should not have the right	Definitely should not have the right	Not at all interested	Never	Never	Never	Never	Daily	Daily	Daily	Daily	Daily	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Never	Never	None	4.0	4.0	Not at all often	Not often	Very often	Very often	Not at all often	Not at all often	Very often	Very often	Very often	Very often	Very important	Not at all	Very good	Fairly good	Very bad	Very bad	Very bad	Left	An essential characteristic of democracy	5	5	An essential characteristic of democracy	5	An essential characteristic of democracy	An essential characteristic of democracy	5	7	5	5	5	No respect at all	Very proud	Very close	Very close	Very close	Very close	Very close	Male	1999.0	22.0	16-24	16-29 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	4	No	Arabic	Single	No children	Upper secondary education (ISCED 3)	Middle	NaN	NaN	Lower secondary education (ISCED 2)	Lower	Lower secondary education (ISCED 2)	Lower	Student	NaN	Never had a job	NaN	Service (for example: restaurant owner, police...	Private business or industry	No	Just get by	Lower middle class	Sixth step	Medium	Muslim	Islam; nfd	MA: Arabe	Disagree	Agree	Agree	Disagree	Disagree	Disagree	Disagree	Agree	Agree	Disagree	Disagree	Disagree	Disagree	Agree	Agree	Disagree	Disagree	Disagree	Agree	Agree	Disagree	Disagree	Agree	Agree	Disagree	Agree	Agree	Agree	Disagree	Agree	Agree	Disagree	Disagree	No trust at all	NaN	NaN	3	Mixed	1	0.776667	0.61	0.518889	0.581111	High	Very low	Low	0.443333	Low	Not religious or atheist	1.0	0.776667	Inconformist	Inconformist	Inconformist	1.0	Low	Very low	Very low	0.886667	Yes	No	No	0.333333	Medium	High	High	0.606667	0.333333	0.777778	0.555556	0.555556	Low	Medio	0.58	0.58	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	1	1	0	1	0
1197	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504071198.0	20211213.0	202111.0	202112.0	16.02	17.1	68.0	Paper-and-Pencil Interviewing (PAPI)	MA-02 L'Oriental	MA: MA-02 L'Oriental	MA: Zaio	20,000-50,000	20000-100000	District center	Urban	81.0	-2.73	34.94	Arabic	ar	Respondent was very interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Very important	Not at all important	Rather important	Not at all important	Rather important	Very important	Important	Not mentioned	Not mentioned	Important	Not mentioned	Not mentioned	Important	Not mentioned	Important	Not mentioned	Important	Mentioned	Not mentioned	Not mentioned	Mentioned	Mentioned	Not mentioned	Mentioned	Mentioned	Not mentioned	Agree strongly	Agree strongly	Disagree	Strongly disagree	Disagree	Agree	Agree	Agree	Agree	Agree	Agree	Agree	Disagree	Agree	Agree	Disagree	Disagree	The entire way our society is organized must b...	Bad thing	Bad thing	Good thing	Quite happy	Fair	6	Completely satisfied	3	Never	Never	Sometimes	Sometimes	Never	Better off	Most people can be trusted	Trust completely	Trust completely	Trust completely	Trust somewhat	Do not trust very much	Do not trust very much	A great deal	A great deal	Not very much	Quite a lot	None at all	Quite a lot	Quite a lot	Not very much	Not very much	Not very much	Quite a lot	Quite a lot	Not very much	Not very much	Not very much	Not very much	Not very much	None at all	Not very much	Not very much	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	4	France	London	Human rights	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Don't belong	4	8	5	5	9	Protecting environment	7	Most of them	Most of them	Most of them	Most of them	Most of them	Frequently	Agree	9	Rather bad	Agree	Disagree	Agree	Disagree	Disagree	Disagree	Agree	Agree	Prohibit people coming here from other countries	Quite secure	Not at all frequently	Not at all frequently	Not at all frequently	Not at all frequently	Not at all frequently	Not at all frequently	Not at all frequently	Yes	Yes	No	Not at all	Not much	No	No	Very much	Very much	Very much	Equality	Security	Yes	A high level of economic growth	Trying to make our cities and countryside more...	Maintaining order in the nation	Fighting rising prices	A stable economy	The fight against crime	5	5	5	7	7	5	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	Never, practically never	Several times a day	A religious person	Do good to other people	Make sense of life in this world	6	9	6	5	5	5	5	5	5	5	5	5	5	5	9	5	5	5	5	7	Definitely should not have the right	Probably should not have the right	Probably should not have the right	Not at all interested	Never	Never	Daily	Never	Never	Never	Never	Never	Never	Would never do	Would never do	Would never do	Would never do	Might do	Might do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Never	Never	None	4.0	4.0	Not at all often	Not often	Very often	Very often	Not at all often	Not at all often	Very often	Very often	Not at all often	Not often	Very important	Not at all	Fairly good	Fairly good	Very bad	Fairly bad	Fairly good	Left	An essential characteristic of democracy	8	4	An essential characteristic of democracy	4	4	4	4	An essential characteristic of democracy	4	5	5	No respect at all	Very proud	Very close	Very close	Very close	Very close	Very close	Female	1957.0	64.0	55-64	50 and more years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	6	No	Arabic	Married	4 children	Early childhood education (ISCED 0) / no educa...	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Homemaker not otherwise employed	Retired/pensioned	Never had a job	Clerical (for example: secretary, clerk, offic...	Sales (for example: sales manager, shop owner,...	Private business or industry	No	Just get by	Lower middle class	Lower step	Low	Muslim	Islam; nfd	MA: Arabe	Disagree	Agree	Agree	Agree	Disagree	Disagree	Agree	Agree	Agree	Agree	Disagree	Agree	Disagree	Agree	Agree	Disagree	Disagree	Disagree	Agree	Disagree	Disagree	Disagree	Agree	Agree	Disagree	Agree	Agree	Agree	Disagree	Agree	Agree	Disagree	Disagree	4	NaN	NaN	Materialist	Materialist	Obedience/Religious Faith	0.388333	0.166667	0.270278	0.540556	Low	Very low	Very low	0.0	Very low	Religious	1.0	0.333333	Inconformist	Inconformist	Inconformist	1.0	Very high	High	High	0.22	No	No	No	0.0	Medium-Low	High	Very high	0.636667	0.444444	0.444444	0.444444	0.444444	Very high	Bajo	0.0	0.0	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	0	0	0
1198	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504071199.0	20211213.0	202111.0	202112.0	17.1	18.02	52.0	Paper-and-Pencil Interviewing (PAPI)	MA-02 L'Oriental	MA: MA-02 L'Oriental	MA: Zaio	20,000-50,000	20000-100000	District center	Urban	81.0	-2.73	34.94	Arabic	ar	Respondent was very interested	There were other people around who could follo...	No weighting	0.833333	31083.33333	Very important	Not very important	Rather important	Not at all important	Very important	Very important	Important	Not mentioned	Important	Important	Not mentioned	Important	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Important	Mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Mentioned	Not mentioned	Agree strongly	Agree strongly	Disagree	Disagree	Agree	Disagree	Agree	Agree	Agree	Agree	Agree	Agree	Disagree	Agree	Agree	Disagree	Disagree	Our society must be gradually improved by reforms	Good thing	Good thing	Good thing	Quite happy	Good	9	6	3	Never	Never	Never	Never	Never	Better off	Need to be very careful	Trust somewhat	Do not trust at all	Do not trust very much	Do not trust at all	Do not trust at all	Do not trust at all	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Not very much	Quite a lot	Quite a lot	None at all	None at all	None at all	Quite a lot	Quite a lot	None at all	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Not very much	Not very much	Not very much	Quite a lot	Not very much	Not very much	Not very much	Quite a lot	Not very much	Not very much	Not very much	5	India	Geneva	Human rights	Inactive member	Inactive member	Inactive member	Not a member	Not a member	Don't belong	Not a member	Inactive member	Inactive member	Inactive member	Inactive member	Don't belong	7	7	4	4	4	Protecting environment	8	Most of them	Few of them	Most of them	Few of them	Few of them	Frequently	Strongly agree	7	Quite good	Agree	Agree	Disagree	Agree	Disagree	Agree	Agree	Disagree	Place strict limits on the number of foreigner...	Not very secure	Not frequently	Not at all frequently	Not at all frequently	Not frequently	Not at all frequently	Quite frequently	Quite frequently	Yes	Yes	No	Not much	A great deal	No	No	A good deal	A great deal	A great deal	Equality	Security	Yes	A high level of economic growth	Making sure this country has strong defence fo...	Maintaining order in the nation	Protecting freedom of speech	A stable economy	Progress toward a less impersonal and more hum...	6	6	4	4	5	7	9	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	Never, practically never	Several times a day	A religious person	Do good to other people	Make sense of life in this world	7	7	7	4	6	7	7	4	4	4	4	4	4	4	6	4	4	4	4	6	Definitely should have the right	Probably should have the right	Probably should have the right	Not at all interested	Never	Never	Weekly	Less than monthly	Weekly	Never	Weekly	Weekly	Monthly	Would never do	Might do	Might do	Might do	Have done	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Never	Never	None	4.0	4.0	Fairly often	Not often	Fairly often	Fairly often	Fairly often	Fairly often	Fairly often	Not often	Fairly often	Fairly often	Very important	Not at all	Very good	Fairly good	Very bad	Fairly bad	Fairly good	Left	9	6	4	9	4	9	9	6	9	6	8	3	No respect at all	Quite proud	Very close	Very close	Very close	Very close	Very close	Female	1978.0	43.0	35-44	30-49 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	4	No	Arabic	Married	2 children	Lower secondary education (ISCED 2)	Lower	Post-secondary non-tertiary education (ISCED 4)	Middle	Primary education (ISCED 1)	Lower	Primary education (ISCED 1)	Lower	Homemaker not otherwise employed	Part time (less than 30 hours a week)	Never had a job	Clerical (for example: secretary, clerk, offic...	Skilled worker (for example: foreman, motor me...	Private business or industry	No	Just get by	Lower middle class	Third step	Low	Muslim	Islam; nfd	MA: Arabe	Disagree	Agree	Agree	Disagree	Disagree	Disagree	Disagree	Agree	Agree	Disagree	Disagree	Disagree	Disagree	Agree	Agree	Disagree	Disagree	Disagree	Agree	Agree	Disagree	Disagree	Agree	Agree	Disagree	Agree	Agree	Agree	Disagree	Agree	Agree	Disagree	Disagree	No trust at all	NaN	NaN	2	Mixed	-1	0.443333	0.221667	0.283194	0.483889	Low	Low	Very low	0.11	Very low	Religious	1.0	0.333333	Inconformist	Inconformist	Inconformist	1.0	High	High	High	0.33	No	No	No	0.0	Medium-Low	High	High	0.523333	0.666667	0.333333	0.333333	0.444444	High	Bajo	0.165	0.165	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	1	0	1
1199	0.0	0.0	2021	Morocco	MAR	Morocco	MOR	504071200.0	20211213.0	202111.0	202112.0	17.4	18.13	34.0	Paper-and-Pencil Interviewing (PAPI)	MA-02 L'Oriental	MA: MA-02 L'Oriental	MA: Zaio	20,000-50,000	20000-100000	District center	Urban	81.0	-2.73	34.94	Arabic	ar	Respondent was very interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Very important	Very important	Rather important	Not at all important	Very important	Very important	Important	Not mentioned	Important	Important	Not mentioned	Not mentioned	Important	Important	Not mentioned	Not mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Agree strongly	Disagree	Agree strongly	Disagree	Disagree	Agree strongly	Disagree	Disagree	Agree strongly	Agree	Neither agree nor disagree	Neither agree nor disagree	Agree strongly	Agree strongly	Strongly agree	Strongly agree	Agree strongly	Our society must be gradually improved by reforms	Bad thing	Good thing	Don't mind	Quite happy	Very good	7	8	7	Never	Sometimes	Never	Never	Never	Better off	Need to be very careful	Trust completely	Trust somewhat	Trust completely	Trust somewhat	Trust somewhat	Trust somewhat	Not very much	A great deal	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Quite a lot	Quite a lot	Quite a lot	Not very much	Quite a lot	Not very much	Not very much	Not very much	Not very much	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Not very much	Not very much	5	China	Geneva	Destruction of historic monuments	Inactive member	Active member	Active member	Not a member	Inactive member	Don't belong	Inactive member	Inactive member	Inactive member	Inactive member	Active member	Don't belong	There should be greater incentives for individ...	Government ownership of business should be inc...	8	Competition is good	In the long run, hard work usually brings a be...	Economy growth and creating jobs	8	Most of them	Few of them	Most of them	Most of them	Most of them	Always	Disagree	4	Neither good, nor bad	Agree	Agree	Hard to say	Agree	Hard to say	Agree	Hard to say	Agree	Let people come as long as there are jobs avai...	Not very secure	Quite frequently	Very Frequently	Quite frequently	Quite frequently	Very Frequently	Very Frequently	Very Frequently	Yes	No	No	Not much	Very much	No	Yes	A good deal	Not much	Not at all	Freedom	Security	Yes	A high level of economic growth	Making sure this country has strong defence fo...	Maintaining order in the nation	Fighting rising prices	A stable economy	Progress toward a less impersonal and more hum...	Completely agree	Completely agree	7	7	3	5	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	Less often	Once a year	Not a religious person	Follow religious norms and ceremonies	Make sense of life in this world	9	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	7	7	Never justifiable	Never justifiable	Never justifiable	Never justifiable	8	Never justifiable	Never justifiable	Never justifiable	Never justifiable	8	Probably should have the right	Definitely should not have the right	Probably should have the right	Somewhat interested	Never	Daily	Never	Daily	Daily	Daily	Daily	Daily	Daily	Would never do	Might do	Might do	Might do	Have done	Would never do	Would never do	Would never do	Have done	Might do	Would never do	Would never do	Never	Never	None	4.0	4.0	Not often	Not at all often	Very often	Very often	Not often	Not often	Not at all often	Not often	Not often	Not often	Very important	Very little	Fairly Bad	Fairly good	Very bad	Fairly good	Very good	7	8	9	An essential characteristic of democracy	8	Not an essential characteristic of democracy	9	7	8	9	9	7	5	Fairly much respect	Quite proud	Very close	Close	Close	Not very close	Not very close	Male	1998.0	23.0	16-24	16-29 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	3	No	Arabic	Single	No children	Post-secondary non-tertiary education (ISCED 4)	Middle	NaN	NaN	Lower secondary education (ISCED 2)	Lower	Lower secondary education (ISCED 2)	Lower	Unemployed	NaN	Never had a job	NaN	Service (for example: restaurant owner, police...	Private non-profit organization	No	Just get by	Lower middle class	Seventh step	Medium	Muslim	Islam; nfd	MA: Arabe	Agree	Agree	Agree	Disagree	Disagree	Agree	Disagree strongly	Agree	Agree	Disagree	Disagree strongly	Agree	Agree	Disagree strongly	Agree	Agree	Disagree	Agree	Agree strongly	Agree strongly	Neither agree nor disagree	Disagree	Agree	Agree	Disagree	Disagree	Disagree	Disagree	Disagree	Agree	Agree	Disagree	Disagree	4	2	2	1	Materialist	1	0.331944	0.443889	0.311944	0.457222	Medium	Low	Very low	0.276667	Very low	Not religious or atheist	0.833333	0.611111	Conformist	Conformist	Conformist	0.0	Very high	Low	Low	0.44	No	No	Yes	0.333333	Medium-High	Very low	High	0.47	0.0	0.666667	0.666667	0.444444	Very high	Bajo	0.0	0.0	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	0	0	1

	Highest educational level: Respondent [ISCED 2011] (LV_Q275)	Frequency	Cumulative Frequency	Percentage	Cumulative Percentage
0	Bachelor or equivalent (ISCED 6)	81	81	6.75	6.75
1	Doctoral or equivalent (ISCED 8)	4	85	0.33	7.08
2	Early childhood education (ISCED 0) / no educa...	358	443	29.83	36.92
3	Lower secondary education (ISCED 2)	136	579	11.33	48.25
4	Master or equivalent (ISCED 7)	31	610	2.58	50.83
5	Post-secondary non-tertiary education (ISCED 4)	118	728	9.83	60.67
6	Primary education (ISCED 1)	124	852	10.33	71.00
7	Short-cycle tertiary education (ISCED 5)	164	1016	13.67	84.67
8	Upper secondary education (ISCED 3)	184	1200	15.33	100.00

	Satisfaction with financial situation of household	Frequency	Cumulative Frequency	Percentage	Cumulative Percentage
0	2	25	25	2.08	2.083333
1	3	53	78	4.42	6.500000
2	4	80	158	6.67	13.166667
3	5	199	357	16.58	29.750000
4	6	286	643	23.83	53.583333
5	7	169	812	14.08	67.666667
6	8	183	995	15.25	82.916667
7	9	73	1068	6.08	89.000000
8	1	42	1110	3.50	92.500000
9	10	90	1200	7.50	100.000000

	Feeling of happiness (NV_Q46)	Highest educational level: Respondent [ISCED 2011] (NV_Q275)	Satisfaction with financial situation of household (NV_Q50)
Feeling of happiness (NV_Q46)	1.000000	-0.005158	-0.438542
Highest educational level: Respondent [ISCED 2011] (NV_Q275)	-0.005158	1.000000	-0.060551
Satisfaction with financial situation of household (NV_Q50)	-0.438542	-0.060551	1.000000

Frequancies¶

Now lets see some other plots like violin plot inorder to predict the slope of the correlation (Dependent var: Feeling of happiness Q46, Independent var: Satisfaction with financial situation of household , Highest educational level: Respondent [ISCED 2011])¶

The violin plot reveals that the majority of the population in the sample falls under the category of 'Early childhood education (ISCED 0) / no education,' resulting in a broader base for this category. However, the correlation between the variables remains somewhat unclear.¶

A clear upward-sloping pattern can be observed for the satisfaction with the financial situation of the household, indicating a positive correlation between satisfaction with the financial situation and the feeling of happiness.¶

Now lets see the correlation matrix¶

There appears to be an issue, as the correlation between "Satisfaction with financial situation of household (NV_Q50)" and "Feeling of happiness (NV_Q46)" is expected to be positive based on previous observations, but it is displayed as negative in the correlation matrix.¶

Fixing the "Feeling of Happiness" Scale¶

With this adjustment, the analysis is now based on the corrected "Feeling of happiness (NV_Q46)_reversed" values, ensuring that the results are more accurate and meaningful.¶

Additionally, the issue of multicollinearity is being examined as part of the analysis.¶

Now it regression time¶

Conclusion from Regression Analysis¶

Key Findings:¶

Conclusion:¶

Conclusion:¶

Mean Feeling of happiness (reversed) for every region¶

showing the rating of the regions based on mean of Satisfaction with financial situation of household for every region¶

Regional Differences in the Relationship Between Financial Satisfaction and Happiness¶

	version	doi	A_YEAR	B_COUNTRY	B_COUNTRY_ALPHA	C_COW_NUM	C_COW_ALPHA	D_INTERVIEW	J_INTDATE	FW_START	FW_END	K_TIME_START	K_TIME_END	K_DURATION	Q_MODE	N_REGION_ISO	N_REGION_WVS	N_TOWN	G_TOWNSIZE	G_TOWNSIZE2	H_SETTLEMENT	H_URBRURAL	I_PSU	O1_LONGITUDE	O2_LATITUDE	S_INTLANGUAGE	LNGE_ISO	E_RESPINT	F_INTPRIVACY	W_WEIGHT	S018	PWGHT	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20	Q21	Q22	Q23	Q24	Q25	Q26	Q27	Q28	Q29	Q30	Q31	Q32	Q33	Q33_3	Q34	Q34_3	Q35	Q35_3	Q37	Q38	Q39	Q40	Q41	Q42	Q43	Q44	Q45	Q46	Q47	Q48	Q49	Q50	Q51	Q52	Q53	Q54	Q55	Q56	Q57	Q58	Q59	Q60	Q61	Q62	Q63	Q64	Q65	Q66	Q67	Q68	Q69	Q70	Q71	Q72	Q73	Q74	Q75	Q76	Q77	Q78	Q79	Q80	Q81	Q82	Q82_ARABLEAGUE	Q82_GULFCOOP	Q82_ISLCOOP	Q83	Q84	Q85	Q86	Q87	Q88	Q89	Q90	Q91	Q92	Q93	Q94	Q95	Q96	Q97	Q98	Q99	Q100	Q101	Q102	Q103	Q104	Q105	Q106	Q107	Q108	Q109	Q110	Q111	Q112	Q113	Q114	Q115	Q116	Q117	Q118	Q119	Q120	Q121	Q122	Q123	Q124	Q125	Q126	Q127	Q128	Q129	Q130	Q131	Q132	Q133	Q134	Q135	Q136	Q137	Q138	Q139	Q140	Q141	Q142	Q143	Q144	Q145	Q146	Q147	Q148	Q149	Q150	Q151	Q152	Q153	Q154	Q155	Q156	Q157	Q158	Q159	Q160	Q161	Q162	Q163	Q164	Q165	Q166	Q167	Q168	Q169	Q170	Q171	Q172	Q173	Q174	Q175	Q176	Q177	Q178	Q179	Q180	Q181	Q182	Q183	Q184	Q185	Q186	Q187	Q188	Q189	Q190	Q191	Q192	Q193	Q194	Q195	Q196	Q197	Q198	Q199	Q200	Q201	Q202	Q203	Q204	Q205	Q206	Q207	Q208	Q209	Q210	Q211	Q212	Q213	Q214	Q215	Q216	Q217	Q218	Q219	Q220	Q221	Q222	Q223	Q223_ABREV	Q223_LOCAL	Q224	Q225	Q226	Q227	Q228	Q229	Q230	Q231	Q232	Q233	Q234	Q234A	Q235	Q236	Q237	Q238	Q239	Q240	Q241	Q242	Q243	Q244	Q245	Q246	Q247	Q248	Q249	Q250	Q251	Q252	Q253	Q254	Q255	Q256	Q257	Q258	Q259	Q260	Q261	Q262	X003R	X003R2	Q263	Q264	Q265	Q266	Q267	Q268	Q269	Q270	Q271	Q272	Q273	Q274	Q275	Q275R	Q276	Q276R	Q277	Q277R	Q278	Q278R	Q279	Q280	Q281	Q282	Q283	Q284	Q285	Q286	Q287	Q288	Q288R	Q289	Q289CS9	Q290	Q291G1	Q291G2	Q291G3	Q291G4	Q291G5	Q291G6	Q291P1	Q291P2	Q291P3	Q291P4	Q291P5	Q291P6	Q291UN1	Q291UN2	Q291UN3	Q291UN4	Q291UN5	Q291UN6	Q292A	Q292B	Q292C	Q292D	Q292E	Q292F	Q292G	Q292H	Q292I	Q292J	Q292K	Q292L	Q292M	Q292N	Q292O	Q293	Q294A	Q294B	Y001	Y002	Y003	SACSECVAL	SACSECVALB	RESEMAVAL	RESEMAVALB	I_AUTHORITY	I_NATIONALISM	I_DEVOUT	DEFIANCE	I_RELIGIMP	I_RELIGBEL	I_RELIGPRAC	DISBELIEF	I_NORM1	I_NORM2	I_NORM3	RELATIVISM	I_TRUSTARMY	I_TRUSTPOLICE	I_TRUSTCOURTS	SCEPTICISM	I_INDEP	I_IMAGIN	I_NONOBED	AUTONOMY	I_WOMJOB	I_WOMPOL	I_WOMEDU	EQUALITY	I_HOMOLIB	I_ABORTLIB	I_DIVORLIB	CHOICE	I_VOICE1	I_VOICE2	I_VOI2_00	VOICE	SECVALWGT	WEIGHT1A	WEIGHT1B	WEIGHT2A	WEIGHT2B	WEIGHT3A	WEIGHT3B	WEIGHT4A	WEIGHT4B	RESEMAVALBWGT	RESEMAVALWGT	SECVALBWGT	Y001_1	Y001_2	Y001_3	Y001_4	Y001_5
0	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504070001.0	20211112.0	202111.0	202112.0	10.05	10.43	38.0	2.0	504009.0	504019.0	504099.0	1.0	1.0	4.0	2.0	99.0	-8.84	29.49	170.0	ar	1.0	2.0	1.0	0.833333	31083.33333	1.0	2.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	2.0	2.0	2.0	2.0	1.0	2.0	1.0	1.0	2.0	2.0	1.0	1.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	3.0	1.0	1.0	2.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0	3.0	2.0	2.0	1.0	3.0	2.0	3.0	5.0	8.0	6.0	4.0	3.0	4.0	4.0	4.0	3.0	2.0	1.0	3.0	2.0	4.0	4.0	2.0	2.0	3.0	3.0	2.0	3.0	3.0	1.0	4.0	4.0	4.0	3.0	2.0	1.0	1.0	3.0	1.0	2.0	3.0	2.0	2.0	2.0	2.0	2.0	3.0	2.0	3.0	4.0	4.0	2.0	5.0	3.0	3.0	2.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	4.0	5.0	5.0	5.0	2.0	10.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	8.0	3.0	2.0	2.0	1.0	2.0	1.0	2.0	1.0	0.0	3.0	2.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	1.0	2.0	1.0	2.0	2.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	2.0	1.0	3.0	1.0	4.0	2.0	4.0	6.0	8.0	8.0	5.0	6.0	8.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	2.0	1.0	4.0	5.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	7.0	2.0	1.0	6.0	2.0	8.0	1.0	1.0	1.0	3.0	10.0	1.0	4.0	4.0	2.0	3.0	5.0	1.0	2.0	2.0	4.0	2.0	2.0	5.0	1.0	1.0	3.0	3.0	2.0	3.0	2.0	3.0	2.0	3.0	3.0	3.0	2.0	2.0	504011.0	504011.0	504011.0	1.0	3.0	2.0	1.0	1.0	1.0	1.0	4.0	4.0	3.0	2.0	5.0	4.0	4.0	4.0	2.0	1.0	8.0	10.0	7.0	6.0	9.0	4.0	8.0	9.0	5.0	10.0	10.0	3.0	1.0	3.0	3.0	4.0	4.0	4.0	3.0	4.0	2.0	1969.0	52.0	4.0	3.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	8.0	1.0	500.0	1.0	4.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	5.0	5.0	9.0	9.0	2.0	1.0	3.0	2.0	6.0	2.0	5.0	50000000.0	504005.0	5.0	2.0	5.0	4.0	5.0	5.0	5.0	2.0	5.0	4.0	5.0	4.0	3.0	4.0	4.0	2.0	5.0	3.0	5.0	4.0	3.0	2.0	2.0	4.0	2.0	2.0	1.0	1.0	1.0	5.0	4.0	1.0	3.0	1.0	1.0	1.0	3.0	2.0	-2.0	0.331667	0.443333	0.235139	0.262778	1.0	0.66	0.00	0.553333	0.00	1.0	0.000000	0.333333	0.0	0.0	0.0	0.000000	0.66	0.66	0.00	0.440000	0.0	0.0	0.0	0.000000	0.25	0.00	0.66	0.303333	0.000000	0.000000	0.666667	0.222222	0.33	0.5	0.415	0.415	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	1.0	1.0	0.0
1	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504070002.0	20211112.0	202111.0	202112.0	10.46	11.19	33.0	2.0	504009.0	504019.0	504099.0	1.0	1.0	4.0	2.0	99.0	-8.84	29.49	170.0	ar	1.0	1.0	1.0	0.833333	31083.33333	1.0	2.0	3.0	4.0	1.0	1.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	2.0	2.0	2.0	1.0	1.0	1.0	2.0	2.0	2.0	2.0	1.0	1.0	1.0	3.0	1.0	1.0	1.0	1.0	1.0	1.0	5.0	2.0	1.0	1.0	1.0	1.0	1.0	3.0	3.0	1.0	1.0	4.0	4.0	5.0	4.0	2.0	4.0	1.0	1.0	2.0	4.0	3.0	2.0	1.0	3.0	4.0	4.0	4.0	3.0	1.0	1.0	4.0	4.0	3.0	1.0	3.0	4.0	4.0	4.0	3.0	3.0	4.0	4.0	4.0	2.0	2.0	1.0	3.0	3.0	3.0	2.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	5.0	3.0	2.0	2.0	2.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	10.0	9.0	6.0	6.0	1.0	10.0	3.0	4.0	3.0	1.0	4.0	4.0	0.0	10.0	1.0	2.0	2.0	0.0	0.0	2.0	2.0	2.0	2.0	4.0	2.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	4.0	1.0	3.0	2.0	3.0	10.0	10.0	10.0	10.0	8.0	1.0	1.0	1.0	2.0	2.0	2.0	4.0	4.0	5.0	5.0	1.0	2.0	2.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	8.0	5.0	5.0	5.0	5.0	9.0	5.0	5.0	5.0	5.0	6.0	1.0	4.0	4.0	2.0	2.0	4.0	4.0	5.0	1.0	5.0	1.0	1.0	4.0	2.0	1.0	2.0	2.0	1.0	2.0	2.0	3.0	2.0	2.0	3.0	3.0	3.0	3.0	504009.0	504009.0	504009.0	4.0	3.0	4.0	4.0	4.0	4.0	1.0	1.0	3.0	2.0	1.0	5.0	1.0	1.0	4.0	1.0	1.0	1.0	10.0	10.0	10.0	10.0	10.0	10.0	10.0	10.0	5.0	8.0	6.0	7.0	3.0	1.0	1.0	1.0	2.0	3.0	4.0	2.0	1998.0	23.0	1.0	1.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	6.0	2.0	500.0	6.0	0.0	6.0	3.0	3.0	2.0	0.0	1.0	0.0	1.0	1.0	NaN	4.0	NaN	6.0	3.0	2.0	2.0	4.0	7.0	2.0	5.0	50000000.0	504005.0	5.0	1.0	5.0	5.0	5.0	5.0	1.0	1.0	1.0	5.0	5.0	5.0	1.0	5.0	3.0	3.0	3.0	2.0	5.0	5.0	1.0	1.0	5.0	4.0	2.0	1.0	1.0	1.0	1.0	1.0	5.0	1.0	1.0	0.0	NaN	NaN	2.0	1.0	-1.0	0.360556	0.111111	0.360556	0.387778	0.0	0.00	0.00	0.000000	0.00	0.0	0.666667	0.222222	1.0	1.0	1.0	1.000000	0.00	0.00	0.66	0.220000	0.0	1.0	1.0	0.666667	0.00	0.00	0.66	0.220000	0.444444	0.444444	0.777778	0.555556	0.00	0.0	0.000	0.000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	1.0
2	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504070003.0	20211112.0	202111.0	202112.0	11.28	12.09	41.0	2.0	504009.0	504019.0	504099.0	1.0	1.0	4.0	2.0	99.0	-8.84	29.49	170.0	ar	1.0	2.0	1.0	0.833333	31083.33333	1.0	2.0	1.0	4.0	1.0	1.0	1.0	2.0	1.0	1.0	2.0	1.0	2.0	1.0	1.0	2.0	2.0	1.0	2.0	2.0	2.0	1.0	2.0	1.0	2.0	2.0	1.0	1.0	1.0	3.0	2.0	3.0	3.0	3.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	3.0	1.0	1.0	3.0	4.0	2.0	2.0	3.0	3.0	3.0	2.0	1.0	1.0	2.0	2.0	1.0	4.0	4.0	4.0	4.0	4.0	2.0	1.0	2.0	3.0	4.0	1.0	1.0	3.0	3.0	4.0	1.0	2.0	4.0	1.0	1.0	1.0	3.0	3.0	4.0	4.0	4.0	4.0	3.0	4.0	4.0	4.0	1.0	2.0	1.0	9.0	3.0	3.0	2.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	1.0	1.0	10.0	2.0	3.0	2.0	2.0	3.0	4.0	4.0	10.0	4.0	0.0	2.0	2.0	2.0	0.0	2.0	2.0	0.0	2.0	2.0	3.0	4.0	4.0	2.0	3.0	3.0	1.0	1.0	1.0	2.0	4.0	2.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0	2.0	1.0	4.0	2.0	3.0	8.0	8.0	7.0	5.0	2.0	7.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	2.0	1.0	1.0	4.0	7.0	2.0	2.0	2.0	1.0	1.0	1.0	4.0	7.0	2.0	1.0	1.0	5.0	3.0	1.0	1.0	1.0	1.0	1.0	1.0	3.0	3.0	4.0	3.0	5.0	5.0	5.0	5.0	5.0	5.0	2.0	2.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	2.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	1.0	4.0	2.0	3.0	3.0	1.0	2.0	3.0	3.0	1.0	3.0	4.0	1.0	1.0	3.0	2.0	2.0	8.0	10.0	7.0	10.0	10.0	5.0	10.0	6.0	8.0	10.0	10.0	6.0	6.0	2.0	2.0	1.0	2.0	4.0	4.0	4.0	1.0	1986.0	35.0	3.0	2.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	4.0	2.0	170.0	3.0	1.0	6.0	3.0	4.0	2.0	6.0	3.0	6.0	3.0	7.0	NaN	9.0	9.0	9.0	3.0	1.0	2.0	2.0	7.0	2.0	5.0	50000000.0	504005.0	5.0	4.0	1.0	4.0	4.0	1.0	5.0	5.0	2.0	4.0	4.0	2.0	2.0	3.0	2.0	3.0	2.0	1.0	5.0	5.0	3.0	2.0	4.0	5.0	1.0	3.0	5.0	4.0	1.0	4.0	4.0	4.0	2.0	7.0	NaN	NaN	3.0	2.0	0.0	0.291389	0.249444	0.304583	0.360000	0.0	0.33	0.00	0.110000	0.00	1.0	0.166667	0.388889	1.0	1.0	0.0	0.666667	0.00	0.00	0.00	0.000000	0.0	0.0	1.0	0.333333	0.50	0.00	0.66	0.386667	0.000000	0.333333	0.666667	0.333333	0.33	0.0	0.165	0.165	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	1.0	1.0	1.0
3	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504070004.0	20211112.0	202111.0	202112.0	12.08	12.42	34.0	2.0	504009.0	504019.0	504099.0	1.0	1.0	4.0	2.0	99.0	-8.84	29.49	170.0	ar	1.0	1.0	1.0	0.833333	31083.33333	1.0	1.0	4.0	2.0	1.0	1.0	1.0	2.0	1.0	2.0	2.0	1.0	2.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	2.0	1.0	2.0	2.0	2.0	2.0	1.0	1.0	1.0	3.0	3.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	3.0	3.0	1.0	1.0	2.0	1.0	8.0	1.0	6.0	4.0	3.0	4.0	4.0	4.0	3.0	2.0	1.0	3.0	3.0	4.0	3.0	3.0	3.0	1.0	4.0	4.0	4.0	1.0	1.0	3.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	3.0	4.0	4.0	4.0	10.0	3.0	3.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	10.0	10.0	10.0	10.0	2.0	7.0	2.0	3.0	2.0	2.0	3.0	3.0	1.0	10.0	4.0	2.0	0.0	1.0	1.0	0.0	0.0	2.0	0.0	3.0	1.0	3.0	2.0	2.0	4.0	2.0	3.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	2.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0	4.0	1.0	3.0	1.0	3.0	10.0	10.0	10.0	8.0	1.0	10.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	2.0	1.0	5.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	4.0	4.0	2.0	2.0	4.0	1.0	4.0	1.0	1.0	1.0	1.0	1.0	3.0	1.0	3.0	3.0	1.0	2.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	2.0	4.0	4.0	4.0	3.0	3.0	1.0	1.0	3.0	2.0	2.0	2.0	1.0	2.0	1.0	3.0	1.0	4.0	4.0	4.0	3.0	5.0	10.0	10.0	10.0	10.0	1.0	10.0	10.0	1.0	1.0	10.0	6.0	6.0	1.0	1.0	2.0	4.0	4.0	4.0	4.0	1.0	1993.0	28.0	2.0	1.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	4.0	2.0	170.0	1.0	2.0	1.0	1.0	0.0	1.0	2.0	1.0	2.0	1.0	7.0	1.0	3.0	0.0	9.0	2.0	2.0	2.0	3.0	6.0	2.0	5.0	50000000.0	504005.0	2.0	1.0	1.0	2.0	5.0	3.0	4.0	1.0	1.0	2.0	5.0	4.0	1.0	1.0	1.0	3.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	2.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	5.0	NaN	NaN	1.0	1.0	-1.0	0.000000	0.000000	0.055000	0.110000	0.0	0.00	0.00	0.000000	0.00	0.0	0.000000	0.000000	0.0	0.0	0.0	0.000000	0.00	0.00	0.00	0.000000	0.0	0.0	0.0	0.000000	0.00	0.00	0.66	0.220000	0.000000	0.000000	0.000000	0.000000	0.00	0.0	0.000	0.000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0
4	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504070005.0	20211112.0	202111.0	202112.0	13.18	13.55	37.0	2.0	504009.0	504019.0	504099.0	1.0	1.0	4.0	2.0	99.0	-8.84	29.49	170.0	ar	1.0	1.0	1.0	0.833333	31083.33333	1.0	2.0	3.0	3.0	1.0	1.0	1.0	1.0	2.0	1.0	2.0	2.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	2.0	1.0	1.0	3.0	3.0	2.0	1.0	1.0	1.0	1.0	1.0	3.0	3.0	3.0	2.0	4.0	4.0	3.0	1.0	3.0	2.0	2.0	3.0	2.0	7.0	7.0	4.0	2.0	3.0	2.0	4.0	4.0	3.0	2.0	1.0	3.0	2.0	3.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	3.0	4.0	4.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	5.0	2.0	1.0	3.0	1.0	2.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	6.0	5.0	3.0	1.0	5.0	1.0	8.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	10.0	3.0	2.0	1.0	1.0	1.0	0.0	2.0	0.0	1.0	2.0	1.0	2.0	2.0	4.0	2.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	1.0	2.0	2.0	1.0	1.0	1.0	2.0	2.0	2.0	1.0	2.0	1.0	3.0	1.0	2.0	8.0	10.0	7.0	6.0	1.0	8.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	7.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	3.0	1.0	4.0	1.0	8.0	2.0	1.0	4.0	5.0	10.0	1.0	4.0	4.0	4.0	3.0	5.0	5.0	5.0	5.0	5.0	1.0	1.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	2.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	1.0	2.0	4.0	2.0	2.0	1.0	2.0	3.0	2.0	1.0	3.0	4.0	4.0	3.0	4.0	1.0	1.0	7.0	1.0	3.0	5.0	9.0	6.0	10.0	10.0	3.0	9.0	8.0	5.0	3.0	3.0	3.0	2.0	2.0	3.0	3.0	4.0	1.0	1999.0	22.0	1.0	1.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	5.0	1.0	170.0	6.0	0.0	1.0	1.0	NaN	NaN	3.0	2.0	2.0	1.0	1.0	NaN	5.0	NaN	9.0	3.0	2.0	3.0	4.0	7.0	2.0	5.0	50000000.0	504005.0	2.0	4.0	1.0	1.0	4.0	5.0	4.0	4.0	2.0	2.0	4.0	2.0	4.0	4.0	1.0	4.0	4.0	4.0	5.0	5.0	2.0	2.0	4.0	5.0	1.0	1.0	5.0	5.0	1.0	5.0	5.0	4.0	5.0	2.0	1.0	1.0	1.0	1.0	2.0	0.317500	0.360000	0.276667	0.220000	0.5	0.66	0.00	0.386667	0.00	1.0	0.000000	0.333333	0.0	0.0	0.0	0.000000	0.33	0.66	0.66	0.550000	1.0	0.0	1.0	0.666667	0.00	0.66	0.66	0.440000	0.000000	0.000000	0.000000	0.000000	0.00	0.0	0.000	0.000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1195	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504071196.0	20211213.0	202111.0	202112.0	14.12	15.11	59.0	2.0	504002.0	504012.0	504081.0	5.0	3.0	3.0	1.0	81.0	-2.73	34.94	170.0	ar	1.0	1.0	1.0	0.833333	31083.33333	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	2.0	2.0	1.0	2.0	2.0	2.0	1.0	2.0	2.0	2.0	2.0	2.0	1.0	2.0	2.0	1.0	1.0	2.0	3.0	2.0	2.0	1.0	1.0	1.0	1.0	4.0	2.0	2.0	1.0	1.0	1.0	2.0	2.0	3.0	1.0	1.0	1.0	1.0	10.0	6.0	6.0	4.0	4.0	3.0	3.0	4.0	1.0	1.0	1.0	2.0	1.0	2.0	1.0	1.0	2.0	2.0	3.0	3.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	3.0	3.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	3.0	3.0	10.0	3.0	1.0	2.0	1.0	2.0	1.0	1.0	2.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	10.0	5.0	10.0	5.0	5.0	2.0	5.0	2.0	2.0	2.0	2.0	2.0	3.0	3.0	4.0	4.0	0.0	2.0	0.0	0.0	0.0	2.0	0.0	0.0	1.0	2.0	4.0	4.0	4.0	4.0	4.0	3.0	3.0	1.0	2.0	2.0	1.0	1.0	2.0	2.0	1.0	1.0	1.0	1.0	2.0	1.0	3.0	2.0	2.0	4.0	2.0	3.0	9.0	9.0	9.0	9.0	4.0	8.0	10.0	1.0	1.0	1.0	1.0	3.0	2.0	7.0	3.0	2.0	2.0	2.0	4.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	6.0	6.0	6.0	5.0	5.0	5.0	5.0	4.0	4.0	6.0	5.0	6.0	2.0	2.0	2.0	1.0	1.0	5.0	2.0	5.0	1.0	3.0	2.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	2.0	1.0	1.0	1.0	2.0	2.0	1.0	1.0	504004.0	504004.0	504004.0	1.0	4.0	2.0	2.0	2.0	1.0	2.0	3.0	1.0	1.0	1.0	3.0	3.0	3.0	3.0	1.0	3.0	10.0	7.0	5.0	7.0	6.0	6.0	7.0	6.0	7.0	6.0	10.0	10.0	10.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	1.0	1980.0	41.0	3.0	2.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	5.0	1.0	170.0	6.0	0.0	4.0	2.0	NaN	NaN	1.0	1.0	1.0	1.0	1.0	NaN	1.0	NaN	6.0	2.0	1.0	2.0	3.0	6.0	2.0	5.0	50000000.0	504005.0	2.0	4.0	4.0	1.0	4.0	2.0	2.0	5.0	2.0	2.0	4.0	2.0	2.0	4.0	4.0	2.0	2.0	2.0	2.0	2.0	4.0	4.0	2.0	2.0	4.0	4.0	2.0	2.0	4.0	2.0	2.0	4.0	4.0	5.0	4.0	4.0	5.0	3.0	2.0	0.526667	0.388333	0.628796	0.424259	0.0	0.33	0.00	0.110000	0.00	1.0	1.000000	0.666667	1.0	1.0	1.0	1.000000	0.33	0.33	0.33	0.330000	1.0	0.0	1.0	0.666667	0.00	0.33	0.66	0.330000	0.444444	0.555556	0.555556	0.518519	1.00	1.0	1.000	1.000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
1196	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504071197.0	20211213.0	202111.0	202112.0	15.30	16.25	55.0	2.0	504002.0	504012.0	504081.0	5.0	3.0	3.0	1.0	81.0	-2.73	34.94	170.0	ar	2.0	1.0	1.0	0.833333	31083.33333	2.0	2.0	2.0	4.0	2.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	1.0	1.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	1.0	2.0	1.0	2.0	2.0	2.0	2.0	3.0	3.0	3.0	3.0	3.0	3.0	1.0	1.0	4.0	2.0	2.0	1.0	1.0	2.0	4.0	1.0	3.0	1.0	3.0	2.0	3.0	7.0	6.0	6.0	3.0	3.0	3.0	2.0	3.0	3.0	2.0	4.0	4.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	4.0	4.0	4.0	3.0	3.0	4.0	3.0	3.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	5.0	1.0	1.0	2.0	1.0	2.0	2.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	10.0	10.0	5.0	5.0	8.0	2.0	10.0	4.0	4.0	4.0	4.0	4.0	4.0	2.0	3.0	3.0	2.0	0.0	2.0	0.0	2.0	2.0	2.0	2.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	1.0	2.0	1.0	1.0	3.0	2.0	3.0	3.0	4.0	10.0	10.0	10.0	10.0	3.0	10.0	6.0	1.0	1.0	1.0	1.0	3.0	2.0	7.0	7.0	2.0	1.0	1.0	5.0	8.0	4.0	4.0	4.0	4.0	4.0	8.0	8.0	6.0	6.0	6.0	4.0	6.0	6.0	4.0	4.0	6.0	4.0	6.0	4.0	4.0	4.0	4.0	3.0	5.0	5.0	5.0	1.0	1.0	1.0	1.0	1.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	4.0	3.0	1.0	1.0	4.0	4.0	1.0	1.0	1.0	1.0	1.0	5.0	1.0	2.0	4.0	4.0	4.0	1.0	10.0	5.0	5.0	10.0	5.0	10.0	10.0	5.0	7.0	5.0	5.0	5.0	4.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1999.0	22.0	1.0	1.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	4.0	1.0	170.0	6.0	0.0	3.0	2.0	NaN	NaN	2.0	1.0	2.0	1.0	6.0	NaN	0.0	NaN	5.0	2.0	2.0	2.0	3.0	6.0	2.0	5.0	50000000.0	504005.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	2.0	4.0	4.0	4.0	2.0	4.0	4.0	2.0	2.0	0.0	NaN	NaN	3.0	2.0	1.0	0.776667	0.610000	0.518889	0.581111	1.0	0.00	0.33	0.443333	0.33	1.0	1.000000	0.776667	1.0	1.0	1.0	1.000000	0.66	1.00	1.00	0.886667	1.0	0.0	0.0	0.333333	0.50	0.66	0.66	0.606667	0.333333	0.777778	0.555556	0.555556	0.66	0.5	0.580	0.580	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	1.0	0.0
1197	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504071198.0	20211213.0	202111.0	202112.0	16.02	17.10	68.0	2.0	504002.0	504012.0	504081.0	5.0	3.0	3.0	1.0	81.0	-2.73	34.94	170.0	ar	1.0	1.0	1.0	0.833333	31083.33333	1.0	4.0	2.0	4.0	2.0	1.0	1.0	2.0	2.0	1.0	2.0	2.0	1.0	2.0	1.0	2.0	1.0	1.0	2.0	2.0	1.0	1.0	2.0	1.0	1.0	2.0	1.0	1.0	3.0	4.0	3.0	2.0	2.0	1.0	2.0	1.0	2.0	1.0	4.0	2.0	2.0	4.0	4.0	1.0	3.0	3.0	1.0	2.0	3.0	6.0	10.0	3.0	4.0	4.0	2.0	2.0	4.0	1.0	1.0	1.0	1.0	1.0	2.0	3.0	3.0	1.0	1.0	3.0	2.0	4.0	2.0	2.0	3.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	3.0	3.0	4.0	3.0	3.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	1.0	2.0	2.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	4.0	8.0	5.0	5.0	9.0	1.0	7.0	3.0	3.0	3.0	3.0	3.0	3.0	2.0	9.0	1.0	2.0	0.0	2.0	0.0	0.0	0.0	2.0	2.0	4.0	2.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	1.0	1.0	2.0	4.0	3.0	2.0	2.0	1.0	1.0	1.0	2.0	2.0	1.0	1.0	4.0	1.0	3.0	1.0	4.0	5.0	5.0	5.0	7.0	7.0	5.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	7.0	1.0	1.0	2.0	2.0	6.0	9.0	6.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	9.0	5.0	5.0	5.0	5.0	7.0	4.0	3.0	3.0	4.0	3.0	5.0	1.0	5.0	5.0	5.0	5.0	5.0	5.0	3.0	3.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	4.0	3.0	1.0	1.0	4.0	4.0	1.0	1.0	4.0	3.0	1.0	5.0	2.0	2.0	4.0	3.0	2.0	1.0	10.0	8.0	4.0	10.0	4.0	4.0	4.0	4.0	10.0	4.0	5.0	5.0	4.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1957.0	64.0	5.0	3.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	6.0	1.0	170.0	1.0	4.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	5.0	4.0	0.0	3.0	4.0	2.0	2.0	2.0	3.0	1.0	1.0	5.0	50000000.0	504005.0	4.0	2.0	2.0	2.0	4.0	4.0	2.0	2.0	2.0	2.0	4.0	2.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	2.0	4.0	4.0	2.0	4.0	4.0	4.0	2.0	4.0	4.0	2.0	2.0	4.0	NaN	NaN	0.0	1.0	-2.0	0.388333	0.166667	0.270278	0.540556	0.0	0.00	0.00	0.000000	0.00	0.0	1.000000	0.333333	1.0	1.0	1.0	1.000000	0.00	0.33	0.33	0.220000	0.0	0.0	0.0	0.000000	0.25	0.66	1.00	0.636667	0.444444	0.444444	0.444444	0.444444	0.00	0.0	0.000	0.000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0
1198	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504071199.0	20211213.0	202111.0	202112.0	17.10	18.02	52.0	2.0	504002.0	504012.0	504081.0	5.0	3.0	3.0	1.0	81.0	-2.73	34.94	170.0	ar	1.0	2.0	1.0	0.833333	31083.33333	1.0	3.0	2.0	4.0	1.0	1.0	1.0	2.0	1.0	1.0	2.0	1.0	2.0	2.0	2.0	2.0	1.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	1.0	2.0	1.0	1.0	3.0	3.0	2.0	3.0	2.0	1.0	2.0	1.0	2.0	1.0	4.0	2.0	2.0	4.0	4.0	2.0	1.0	1.0	1.0	2.0	2.0	9.0	6.0	3.0	4.0	4.0	4.0	4.0	4.0	1.0	2.0	2.0	4.0	3.0	4.0	4.0	4.0	2.0	2.0	2.0	2.0	3.0	2.0	2.0	4.0	4.0	4.0	2.0	2.0	4.0	2.0	2.0	2.0	2.0	2.0	3.0	3.0	3.0	2.0	3.0	3.0	3.0	2.0	3.0	3.0	3.0	5.0	3.0	3.0	2.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	7.0	7.0	4.0	4.0	4.0	1.0	8.0	3.0	2.0	3.0	2.0	2.0	3.0	1.0	7.0	4.0	2.0	2.0	0.0	2.0	0.0	2.0	2.0	0.0	3.0	3.0	3.0	4.0	4.0	3.0	4.0	2.0	2.0	1.0	1.0	2.0	3.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	1.0	1.0	2.0	1.0	4.0	1.0	2.0	6.0	6.0	4.0	4.0	5.0	7.0	9.0	1.0	1.0	1.0	1.0	1.0	1.0	7.0	1.0	1.0	2.0	2.0	7.0	7.0	7.0	4.0	6.0	7.0	7.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	6.0	4.0	4.0	4.0	4.0	6.0	1.0	2.0	2.0	4.0	3.0	5.0	2.0	4.0	2.0	5.0	2.0	2.0	3.0	3.0	2.0	2.0	2.0	1.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	2.0	3.0	2.0	2.0	2.0	2.0	2.0	3.0	2.0	2.0	1.0	5.0	1.0	2.0	4.0	3.0	2.0	1.0	9.0	6.0	4.0	9.0	4.0	9.0	9.0	6.0	9.0	6.0	8.0	3.0	4.0	2.0	1.0	1.0	1.0	1.0	1.0	2.0	1978.0	43.0	3.0	2.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	4.0	1.0	170.0	1.0	2.0	2.0	1.0	4.0	2.0	1.0	1.0	1.0	1.0	5.0	2.0	0.0	3.0	6.0	2.0	2.0	2.0	3.0	3.0	1.0	5.0	50000000.0	504005.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	2.0	4.0	4.0	4.0	2.0	4.0	4.0	2.0	2.0	0.0	NaN	NaN	2.0	2.0	-1.0	0.443333	0.221667	0.283194	0.483889	0.0	0.33	0.00	0.110000	0.00	0.0	1.000000	0.333333	1.0	1.0	1.0	1.000000	0.33	0.33	0.33	0.330000	0.0	0.0	0.0	0.000000	0.25	0.66	0.66	0.523333	0.666667	0.333333	0.333333	0.444444	0.33	0.0	0.165	0.165	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	1.0	0.0	1.0
1199	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504071200.0	20211213.0	202111.0	202112.0	17.40	18.13	34.0	2.0	504002.0	504012.0	504081.0	5.0	3.0	3.0	1.0	81.0	-2.73	34.94	170.0	ar	1.0	1.0	1.0	0.833333	31083.33333	1.0	1.0	2.0	4.0	1.0	1.0	1.0	2.0	1.0	1.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	1.0	2.0	2.0	2.0	1.0	2.0	1.0	2.0	2.0	1.0	3.0	1.0	3.0	3.0	1.0	4.0	2.0	1.0	1.0	3.0	3.0	1.0	1.0	1.0	1.0	1.0	2.0	3.0	1.0	2.0	2.0	1.0	7.0	8.0	7.0	4.0	2.0	4.0	4.0	4.0	1.0	2.0	1.0	2.0	1.0	2.0	2.0	2.0	3.0	1.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	2.0	2.0	2.0	3.0	2.0	3.0	3.0	3.0	3.0	2.0	2.0	2.0	2.0	2.0	3.0	3.0	5.0	2.0	3.0	3.0	1.0	2.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	2.0	0.0	10.0	10.0	8.0	1.0	1.0	2.0	8.0	3.0	2.0	3.0	3.0	3.0	4.0	3.0	4.0	3.0	2.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	2.0	3.0	2.0	1.0	2.0	2.0	1.0	1.0	1.0	1.0	2.0	2.0	3.0	1.0	2.0	1.0	2.0	3.0	4.0	1.0	2.0	1.0	1.0	2.0	1.0	3.0	1.0	2.0	10.0	10.0	7.0	7.0	3.0	5.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	6.0	6.0	2.0	1.0	2.0	9.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	7.0	7.0	1.0	1.0	1.0	1.0	8.0	1.0	1.0	1.0	1.0	8.0	2.0	4.0	2.0	2.0	3.0	1.0	5.0	1.0	1.0	1.0	1.0	1.0	1.0	3.0	2.0	2.0	2.0	1.0	3.0	3.0	3.0	1.0	2.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	3.0	4.0	1.0	1.0	3.0	3.0	4.0	3.0	3.0	3.0	1.0	4.0	3.0	2.0	4.0	2.0	1.0	7.0	8.0	9.0	10.0	8.0	1.0	9.0	7.0	8.0	9.0	9.0	7.0	5.0	2.0	2.0	1.0	2.0	2.0	3.0	3.0	1.0	1998.0	23.0	1.0	1.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	3.0	1.0	170.0	6.0	0.0	4.0	2.0	NaN	NaN	2.0	1.0	2.0	1.0	7.0	NaN	0.0	NaN	5.0	3.0	2.0	2.0	3.0	7.0	2.0	5.0	50000000.0	504005.0	2.0	2.0	2.0	4.0	4.0	2.0	5.0	2.0	2.0	4.0	5.0	2.0	2.0	5.0	2.0	2.0	4.0	2.0	5.0	5.0	3.0	2.0	4.0	4.0	2.0	2.0	2.0	2.0	2.0	4.0	4.0	2.0	2.0	4.0	2.0	2.0	1.0	1.0	1.0	0.331944	0.443889	0.311944	0.457222	0.5	0.33	0.00	0.276667	0.00	1.0	0.833333	0.611111	0.0	0.0	0.0	0.000000	0.00	0.66	0.66	0.440000	0.0	0.0	1.0	0.333333	0.75	0.00	0.66	0.470000	0.000000	0.666667	0.666667	0.444444	0.00	0.0	0.000	0.000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0

	Feeling of happiness (LV_Q46)	Feeling of happiness (NV_Q46)	Frequency	Cumulative Frequency	Percentage	Cumulative Percentage
0	Not at all happy	4.0	11	11	0.92	0.916667
1	Not very happy	3.0	195	206	16.25	17.166667
2	Quite happy	2.0	845	1051	70.42	87.583333
3	Very happy	1.0	149	1200	12.42	100.000000

	Feeling of happiness (NV_Q46)_inverse	Highest educational level: Respondent [ISCED 2011] (NV_Q275)	Satisfaction with financial situation of household (NV_Q50)
Feeling of happiness (NV_Q46)_inverse	1.000000	0.010114	0.437768
Highest educational level: Respondent [ISCED 2011] (NV_Q275)	0.010114	1.000000	-0.048808
Satisfaction with financial situation of household (NV_Q50)	0.437768	-0.048808	1.000000

	Feeling of happiness	Highest educational level	Satisfaction with financial situation of household
Feeling of happiness	1.000000	0.010114	0.437768
Highest educational level	0.010114	1.000000	-0.048808
Satisfaction with financial situation of household	0.437768	-0.048808	1.000000

	index	inverse_Q46
387	inverse_Q46	1.000000
57	Q49	0.452586
58	Q50	0.437768
56	Q48	0.394343
62	Q54	0.270797
63	Q55	0.254855
61	Q53	0.247042
59	Q51	0.231129
265	Q251	0.217538
104	Q93	0.213212
266	Q252	0.211310

	index	Q50
58	Q50	1.000000
57	Q49	0.667909
56	Q48	0.495239
62	Q54	0.438604
387	inverse_Q46	0.437768
61	Q53	0.377738
59	Q51	0.313080
60	Q52	0.275484
63	Q55	0.259059
153	Q142	0.240693
301	Q288	0.227805
150	Q139	0.216577
148	Q137	0.212883
221	Q210	0.204108

	id	name	geometry
0	MA11	Laâyoune-Sakia El Hamra	POLYGON ((-8.75659 27.14772, -8.75422 27.14715...
1	MA01	Tangier-Tetouan-Al Hoceima	POLYGON ((-3.82389 35.19936, -3.82346 35.19887...
2	MA02	Oriental	POLYGON ((-3.82012 34.88753, -3.81998 34.89199...
3	MA08	Drâa-Tafilalet	POLYGON ((-4.01816 32.60844, -4.00295 32.57946...
4	MA09	Souss-Massa	POLYGON ((-7.73179 31.13463, -7.73274 31.13365...
5	MA10	Guelmim-Oued Noun	POLYGON ((-8.75659 27.14772, -8.80915 27.16043...
6	MA06	Casablanca-Settat	POLYGON ((-9.05932 32.72101, -9.05904 32.7215,...
7	MA07	Marrakech-Safi	POLYGON ((-7.18452 31.42896, -7.1929 31.42544,...
8	MA12	Dakhla-Oued Ed-Dahab	POLYGON ((-14.90488 24.6832, -14.89861 24.6739...
9	MA04	Rabat-Salé-Kenitra	POLYGON ((-6.2434 35.00139, -6.23397 34.99872,...
10	MA03	Fez-Meknes	POLYGON ((-5.31515 34.5159, -5.28082 34.51478,...
11	MA05	Béni Mellal-Khénifra	POLYGON ((-5.25455 32.86937, -5.25284 32.86344...

	ID_list	N_REGION_ISO	Feeling of happiness (NV_Q46)_reversed
0	MA01	MA-01 Tanger-Tetouan-Al Hoceima	2.941667
1	MA02	MA-02 L'Oriental	3.137500
2	MA03	MA-03 Fes-Meknes	3.020000
3	MA04	MA-04 Rabat-Sale-Kenitra	2.911765
4	MA05	MA-05 Beni Mellal-Khenifra	2.812500
5	MA06	MA-06 Casablanca-Settat	2.934615
6	MA07	MA-07 Marrakech-Safi	2.893333
7	MA08	MA-08 Draa-Tafilalet	2.980000
8	MA09	MA-09 Souss-Massa	2.940000
9	MA10	MA-10 Guelmim-Oued Noun	2.900000
10	MA11	MA-11 Laayoune-Sakia El Hamra (EH-partial)	2.800000
11	MA12	MA-12 Dakhla-Oued Ed-Dahab (EH)	3.000000

	ID_list	N_REGION_ISO	Satisfaction with financial situation of household (NV_Q50)
0	MA01	MA-01 Tanger-Tetouan-Al Hoceima	6.508333
1	MA02	MA-02 L'Oriental	6.262500
2	MA03	MA-03 Fes-Meknes	6.766667
3	MA04	MA-04 Rabat-Sale-Kenitra	6.141176
4	MA05	MA-05 Beni Mellal-Khenifra	5.975000
5	MA06	MA-06 Casablanca-Settat	6.080769
6	MA07	MA-07 Marrakech-Safi	6.033333
7	MA08	MA-08 Draa-Tafilalet	6.340000
8	MA09	MA-09 Souss-Massa	6.110000
9	MA10	MA-10 Guelmim-Oued Noun	6.200000
10	MA11	MA-11 Laayoune-Sakia El Hamra (EH-partial)	6.850000
11	MA12	MA-12 Dakhla-Oued Ed-Dahab (EH)	5.400000

	ID_list	regions_name	N_REGION_ISO	mean_FoH	mean_SFSH	count
0	MA01	Tanger-Tetouan-Al	MA-01 Tanger-Tetouan-Al Hoceima	2.941667	6.508333	120
1	MA02	L'Oriental	MA-02 L'Oriental	3.137500	6.262500	80
2	MA03	Fes-Meknes	MA-03 Fes-Meknes	3.020000	6.766667	150
3	MA04	Rabat-Sale-Kenitra	MA-04 Rabat-Sale-Kenitra	2.911765	6.141176	170
4	MA05	Beni	MA-05 Beni Mellal-Khenifra	2.812500	5.975000	80
5	MA06	Casablanca-Settat	MA-06 Casablanca-Settat	2.934615	6.080769	260
6	MA07	Marrakech-Safi	MA-07 Marrakech-Safi	2.893333	6.033333	150
7	MA08	Draa-Tafilalet	MA-08 Draa-Tafilalet	2.980000	6.340000	50
8	MA09	Souss-Massa	MA-09 Souss-Massa	2.940000	6.110000	100
9	MA10	Guelmim-Oued	MA-10 Guelmim-Oued Noun	2.900000	6.200000	10
10	MA11	Laayoune-Sakia	MA-11 Laayoune-Sakia El Hamra (EH-partial)	2.800000	6.850000	20
11	MA12	Dakhla-Oued	MA-12 Dakhla-Oued Ed-Dahab (EH)	3.000000	5.400000	10

	H_SETTLEMENT	H_URBRURAL	I_PSU	O1_LONGITUDE	O2_LATITUDE	E_RESPINT	F_INTPRIVACY	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20	Q21	Q22	Q23	Q24	Q25	Q26	Q27	Q28	Q29	Q30	Q31	Q32	Q33	Q33_3	Q34	Q34_3	Q35	Q35_3	Q37	Q38	Q39	Q40	Q41	Q42	Q43	Q44	Q45	Q46	Q47	Q48	Q49	Q50	Q51	Q52	Q53	Q54	Q55	Q56	Q57	Q58	Q59	Q60	Q61	Q62	Q63	Q64	Q65	Q66	Q67	Q68	Q69	Q70	Q71	Q72	Q73	Q74	Q75	Q76	Q77	Q78	Q79	Q80	Q81	Q82	Q82_ARABLEAGUE	Q82_GULFCOOP	Q82_ISLCOOP	Q83	Q84	Q85	Q86	Q87	Q88	Q89	Q90	Q91	Q92	Q93	Q94	Q95	Q96	Q97	Q98	Q99	Q100	Q101	Q102	Q103	Q104	Q105	Q106	Q107	Q108	Q109	Q110	Q111	Q112	Q113	Q114	Q115	Q116	Q117	Q118	Q119	Q120	Q121	Q122	Q123	Q124	Q125	Q126	Q127	Q128	Q129	Q130	Q131	Q132	Q133	Q134	Q135	Q136	Q137	Q138	Q139	Q140	Q141	Q142	Q143	Q144	Q145	Q146	Q147	Q148	Q149	Q150	Q151	Q152	Q153	Q154	Q155	Q156	Q157	Q158	Q159	Q160	Q161	Q162	Q163	Q164	Q165	Q166	Q167	Q168	Q169	Q170	Q171	Q172	Q173	Q174	Q175	Q176	Q177	Q178	Q179	Q180	Q181	Q182	Q183	Q184	Q185	Q186	Q187	Q188	Q189	Q190	Q191	Q192	Q193	Q194	Q195	Q196	Q197	Q198	Q199	Q200	Q201	Q202	Q203	Q204	Q205	Q206	Q207	Q208	Q209	Q210	Q211	Q212	Q213	Q214	Q215	Q216	Q217	Q218	Q219	Q220	Q221	Q222	Q223	Q223_ABREV	Q223_LOCAL	Q224	Q225	Q226	Q227	Q228	Q229	Q230	Q231	Q232	Q233	Q234	Q234A	Q235	Q236	Q237	Q238	Q239	Q240	Q241	Q242	Q243	Q244	Q245	Q246	Q247	Q248	Q249	Q250	Q251	Q252	Q253	Q254	Q255	Q256	Q257	Q258	Q259	Q260	Q261	Q262	X003R	X003R2	Q270	Q271	Q272	Q273	Q274	Q275	Q275R	Q276	Q276R	Q277	Q277R	Q278	Q278R	Q279	Q280	Q281	Q282	Q283	Q284	Q285	Q286	Q287	Q288	Q288R	Q289	Q289CS9	Q291G1	Q291G2	Q291G3	Q291G4	Q291G5	Q291G6	Q291P1	Q291P2	Q291P3	Q291P4	Q291P5	Q291P6	Q291UN1	Q291UN2	Q291UN3	Q291UN4	Q291UN5	Q291UN6	Q292A	Q292B	Q292C	Q292D	Q292E	Q292F	Q292G	Q292H	Q292I	Q292J	Q292K	Q292L	Q292M	Q292N	Q292O	Q293	Q294A	Q294B	Y001	Y002	Y003	SACSECVAL	SACSECVALB	RESEMAVAL	RESEMAVALB	I_AUTHORITY	I_NATIONALISM	I_DEVOUT	DEFIANCE	I_RELIGIMP	I_RELIGBEL	I_RELIGPRAC	DISBELIEF	I_NORM1	I_NORM2	I_NORM3	RELATIVISM	I_TRUSTARMY	I_TRUSTPOLICE	I_TRUSTCOURTS	SCEPTICISM	I_INDEP	I_IMAGIN	I_NONOBED	AUTONOMY	I_WOMJOB	I_WOMPOL	I_WOMEDU	EQUALITY	I_HOMOLIB	I_ABORTLIB	I_DIVORLIB	CHOICE	I_VOICE1	I_VOICE2	I_VOI2_00	VOICE	WEIGHT4B	RESEMAVALWGT	Y001_1	Y001_2	Y001_3	Y001_4	Y001_5	inverse_Q46
H_SETTLEMENT	1.000000	0.851557	0.022023	0.103847	-0.116227	-0.095395	0.040084	-0.038877	0.058069	-0.015620	0.034239	0.008605	-0.047738	0.045166	-0.017303	0.032499	-0.006529	-0.049553	0.034531	0.007053	-0.040433	0.061857	-0.117056	-0.014307	0.042674	-0.027864	0.036353	0.053876	0.020055	-0.009750	-0.001316	0.119777	0.006036	-0.091690	-0.081286	-0.064515	-0.023122	-0.085735	-0.077615	-0.097049	-0.089388	-0.156636	-0.085014	-0.112094	-0.087274	-0.046805	-0.136842	-0.023274	-0.047734	0.027271	0.064046	0.031853	0.024404	-0.102237	-0.014024	0.009328	0.117326	0.065581	0.063112	0.063347	-0.020556	-0.007188	0.053780	0.027629	-0.071502	-0.047949	0.022692	-0.003380	-0.075922	-0.056697	-0.048233	-0.060803	-0.106181	-0.122087	-0.038248	-0.088293	-0.065291	-0.139407	-0.087272	0.000352	-0.012642	-0.010987	-0.043075	-0.097329	-0.033689	-0.093685	-0.099943	-0.096002	-0.120830	-0.127532	-0.117685	-0.117685	-0.050426	-0.155186	-0.100435	-0.065413	-0.070906	-0.097334	-0.112939	-0.065259	-0.054638	0.048189	0.078257	0.041376	0.056096	0.019676	0.082128	0.061202	0.010528	-0.007877	-0.047291	-0.015795	0.023023	-0.037813	-0.034872	0.019418	-0.065327	-0.005596	0.105933	0.046680	0.096061	0.085267	-0.052162	0.099447	-0.118804	-0.091175	-0.063991	-0.110112	-0.052518	0.140583	0.056131	0.014610	-0.087602	-0.055024	-0.061175	-0.033641	-0.056101	-0.097086	0.042975	0.050593	-0.074081	0.164338	-0.052497	0.042040	0.020126	0.032790	0.023303	-0.010018	0.016531	0.057287	0.092492	0.070731	0.000425	0.017357	-0.086959	-0.024933	-0.003775	-0.032189	-0.009048	-0.083949	-0.074017	-0.020496	0.044733	-0.029423	-0.033072	-0.086645	0.073746	0.002958	-0.001183	0.057841	0.085734	-0.048577	0.014686	-0.058616	0.111462	0.077060	0.020394	-0.084883	-0.046822	-0.035692	-0.004019	-0.087669	0.019377	0.006953	0.087064	0.026299	-0.083116	0.061598	-0.039962	-0.051815	0.029543	-0.014233	-0.019275	-0.016801	-0.043237	0.081821	0.091757	-0.026994	-0.012886	0.077454	0.005911	0.047798	0.005240	0.027365	0.000759	0.026910	-0.001958	-0.052647	-0.038159	-0.035455	0.028039	0.049684	0.044968	0.069813	0.040002	-0.016773	0.052238	0.025934	0.018914	0.041979	0.035041	0.040239	-0.040038	0.009658	-0.076665	0.018833	0.066730	-0.035838	-0.029597	0.007572	0.059650	0.098316	-0.049786	-0.076122	0.056362	0.056362	0.056362	-0.062370	0.012438	0.132699	-0.141543	-0.131470	-0.095230	-0.119461	0.037769	-0.062400	-0.156662	-0.019919	-0.037288	-0.091421	-0.200674	0.044134	0.011035	-0.034133	-0.027028	0.030852	0.111605	0.107995	0.137522	0.073704	0.057124	0.090475	0.142247	0.074627	0.059050	-0.083776	-0.056849	0.116047	-0.032554	0.157148	0.140267	0.158842	0.164251	0.159950	0.000000	0.031079	-0.029442	-0.030684	-0.032153	0.023859	0.054497	0.002947	0.022724	-0.010107	-0.046622	-0.023183	-0.006014	0.018937	0.025835	0.077499	-0.044120	0.016296	-0.006772	0.067913	0.500790	0.406905	0.490004	0.115498	0.060025	0.034243	0.144766	0.039899	-0.017806	-0.020394	0.020394	0.059926	-0.030383	-0.008899	-0.053868	0.137074	0.011531	-0.045238	0.003709	0.060319	-0.069814	0.069633	-0.021967	0.019232	0.041862	0.031887	-0.041202	0.090315	-0.047875	0.028628	0.031524	0.077678	0.018766	-0.056070	-0.050271	-0.004500	-0.019040	0.049893	-0.050308	0.013474	-0.020899	-0.031422	-0.004306	0.009430	-0.052771	-0.027919	-0.030017	0.008569	-0.067567	0.039602	-0.073177	0.003240	-0.020635	-0.008144	-0.102237	-0.032554	-0.091690	-0.127415	-0.047738	0.087064	0.019377	0.057728	-0.041371	-0.010698	-0.002606	-0.022181	-0.122087	-0.139407	-0.087272	-0.133369	0.017303	0.049553	-0.014307	0.021425	-0.097049	-0.064515	-0.023122	-0.077039	-0.016801	0.081821	0.091757	0.068763	-0.085561	0.018141	-0.041872	-0.039178	-0.002534	-0.002534	0.031555	-0.091389	-0.001225	0.034508	0.050213	0.014024
H_URBRURAL	0.851557	1.000000	-0.007240	0.049131	-0.159549	-0.146464	0.045706	-0.066152	0.012984	0.015860	0.045106	-0.048630	-0.082980	0.051460	0.031534	0.028702	0.019811	-0.030350	-0.011287	-0.000631	0.014719	0.035223	-0.085126	-0.038197	0.033675	-0.019397	0.006582	0.072326	-0.005888	-0.029784	-0.013945	0.062649	-0.003425	-0.096293	-0.112756	-0.081076	-0.029527	-0.093486	-0.130306	-0.086858	-0.067228	-0.180908	-0.119982	-0.121579	-0.113214	-0.088097	-0.130530	-0.096514	-0.074104	0.002493	0.056565	0.024946	0.021971	-0.128743	-0.062452	-0.003158	0.106089	0.101900	0.100201	0.035848	0.020130	-0.005710	0.077169	0.043365	-0.054792	-0.020753	0.047417	-0.002292	-0.054962	-0.043850	-0.023707	-0.041454	-0.111633	-0.063217	-0.019981	-0.066508	-0.062314	-0.096594	-0.082381	0.004829	-0.046052	-0.037641	-0.038382	-0.093788	-0.035681	-0.080944	-0.049002	-0.067615	-0.114972	-0.090006	-0.079432	-0.079432	-0.025043	-0.136474	-0.073769	-0.019819	-0.026779	-0.066293	-0.079056	-0.042096	-0.009061	0.073158	0.060044	0.037500	0.020198	0.083122	0.131553	0.106891	0.053467	0.035071	0.027573	0.046850	0.079997	0.018711	0.021914	0.080130	0.002825	0.006671	0.134623	0.032411	0.053654	0.048397	-0.111299	0.111312	-0.071793	-0.064424	-0.062487	-0.038514	0.019924	0.162838	0.062315	0.014277	-0.061955	-0.012948	-0.061843	0.012064	-0.041769	-0.104081	0.084169	0.064298	-0.028050	0.132473	-0.127225	0.007864	0.011539	0.001443	-0.024808	-0.000485	0.025624	0.069395	0.110462	0.086984	0.028071	0.021805	-0.070239	-0.015927	0.055380	-0.042817	-0.011334	-0.107514	-0.032738	-0.010829	0.069389	-0.001693	0.007175	-0.117339	0.095094	0.024099	0.027504	0.094516	0.086070	0.011821	0.064511	-0.056815	0.102266	0.116449	-0.001041	-0.070600	-0.057753	-0.049126	0.020457	-0.112459	-0.000436	-0.030402	0.096512	-0.005097	-0.095316	0.052200	0.018396	-0.058054	0.075232	0.040831	0.055036	0.049714	0.011021	0.116482	0.086271	0.038438	0.054477	0.150898	0.041425	0.054914	0.100486	0.131484	0.079563	0.123628	0.012489	-0.031203	-0.004794	0.001911	0.053618	0.093600	0.079559	0.076254	0.028833	-0.035055	0.039582	0.007440	0.029708	0.043042	0.063485	0.115067	0.018557	0.063969	-0.060087	0.071730	0.138709	0.013340	0.001112	0.045834	0.092759	0.151268	0.011333	-0.008818	0.020189	0.020189	0.020189	-0.033240	-0.018563	0.171236	-0.064148	-0.084999	-0.051799	-0.052544	0.073495	0.002529	-0.079020	0.004813	0.026697	-0.064529	-0.227690	0.003786	0.072098	-0.102964	0.047072	-0.001015	0.150349	0.074360	0.106199	0.131452	0.041794	0.074449	0.118275	0.044140	0.064365	-0.045554	-0.031135	0.066632	-0.026027	0.178878	0.176302	0.193148	0.156135	0.136599	0.000000	0.074182	-0.072280	-0.069914	-0.071272	0.042041	0.077525	-0.036717	0.085190	-0.062327	-0.037157	-0.010115	0.018941	0.034707	0.031865	0.080051	-0.018242	0.009398	0.015129	0.085172	0.564214	0.477543	0.578616	0.123015	0.059500	0.015213	0.122976	0.103856	0.025010	0.001041	-0.001041	0.025740	-0.025222	0.032354	-0.042087	0.132103	-0.022952	-0.075327	0.004824	0.071510	-0.080151	0.032829	-0.045574	-0.003211	0.044208	0.041732	-0.029013	0.076128	-0.064646	0.031652	0.009586	0.087785	0.015712	-0.062487	-0.059916	0.013893	-0.006317	0.061307	-0.080158	0.028021	-0.042441	-0.060208	-0.010541	0.008099	-0.050806	0.033583	0.036082	0.013202	-0.048473	-0.023635	-0.031935	-0.011192	-0.038245	0.022095	-0.128743	-0.026027	-0.096293	-0.142248	-0.082980	0.096512	-0.000436	0.048678	-0.035219	0.051686	0.056465	0.021941	-0.063217	-0.096594	-0.082381	-0.089319	-0.031534	0.030350	-0.038197	-0.026677	-0.086858	-0.081076	-0.029527	-0.075246	0.049714	0.116482	0.086271	0.120450	-0.072033	-0.021573	-0.061652	-0.058607	-0.009712	-0.009712	-0.010970	-0.090997	0.019165	0.069050	0.051057	0.062452
I_PSU	0.022023	-0.007240	1.000000	-0.116754	0.306493	-0.055762	-0.050132	-0.039743	-0.013965	0.040565	-0.075490	-0.034709	-0.031265	-0.050992	0.027181	-0.024489	0.101385	-0.031465	0.055939	0.056181	-0.026788	-0.000256	-0.034064	-0.008132	-0.037549	0.012883	-0.061764	-0.013970	-0.011019	0.010018	-0.073382	0.004011	0.000160	0.010783	0.010030	-0.008660	0.017324	0.054051	0.036323	0.014687	0.001278	-0.024563	-0.008668	-0.006749	0.008142	-0.070051	-0.037243	-0.008921	-0.061041	-0.053596	0.068317	0.060913	0.040425	-0.087971	-0.014992	0.033827	-0.047275	-0.013006	-0.022463	0.005561	-0.069875	-0.026826	-0.044894	-0.010940	-0.017578	0.024564	-0.107387	-0.040895	-0.047877	0.014598	0.035581	0.024930	-0.076984	-0.131111	-0.113609	-0.079528	-0.089589	-0.085275	-0.057321	-0.072730	-0.100772	-0.095451	-0.084060	-0.031415	-0.102039	-0.109780	-0.063371	-0.085419	-0.037437	-0.109668	-0.064256	-0.064256	-0.116567	-0.086413	-0.062231	-0.053667	-0.044835	-0.041950	-0.024832	0.028672	0.015453	0.091471	-0.071911	0.043439	-0.046195	-0.015310	-0.049950	-0.008971	0.015952	0.002072	-0.021178	0.013816	-0.025733	0.018730	0.006858	0.006466	0.043076	-0.041861	-0.020827	0.002552	-0.012857	-0.028588	0.000749	-0.087641	-0.053783	0.003071	-0.059687	-0.055619	-0.032019	-0.123415	0.068099	0.010428	-0.047292	0.046575	-0.010959	-0.013679	-0.004804	0.029518	0.040639	-0.008825	0.039455	-0.031709	0.075215	-0.036764	-0.000539	-0.017912	-0.017200	-0.039915	-0.024178	0.008341	-0.073806	-0.061398	-0.029660	-0.092242	-0.041734	-0.053211	-0.024009	-0.069148	-0.009035	0.060695	0.020312	0.043034	0.021196	0.003086	0.053169	-0.012684	0.004146	-0.036438	-0.028653	-0.030699	-0.074967	0.023510	0.017401	0.067146	-0.088533	-0.076777	0.010907	0.030447	0.062629	0.043204	-0.024067	0.053861	-0.100274	-0.022787	-0.056893	-0.107579	0.018800	0.061150	0.071694	0.065845	0.078749	0.081482	0.099417	0.088525	0.087915	0.015189	-0.071740	0.069249	0.072434	0.089868	0.017391	-0.044231	0.047136	0.099404	0.043335	0.049836	0.053438	0.074952	0.072428	0.043045	-0.073443	-0.045809	-0.025166	-0.122479	-0.040680	-0.103170	-0.004947	-0.110789	-0.062813	-0.064972	-0.117105	-0.109224	-0.091461	-0.062747	-0.082633	-0.047662	-0.069341	-0.027647	-0.080280	-0.077304	-0.040800	-0.039954	-0.083738	-0.075628	0.005067	0.005067	0.005067	-0.016354	0.025618	0.019326	0.124905	0.025831	-0.018007	0.098679	-0.019826	0.005069	-0.033980	-0.090651	-0.066050	0.133294	0.112357	0.029149	-0.047982	0.058110	-0.012301	0.113349	-0.052207	-0.034470	0.054531	-0.009303	0.099876	-0.011070	-0.062974	-0.003922	0.034221	0.124739	0.130649	-0.127336	-0.054583	-0.105756	-0.084470	-0.099328	-0.080279	-0.076487	0.000000	0.027885	-0.028227	-0.028486	-0.005572	-0.034786	0.071648	-0.008028	0.006807	-0.018161	0.017932	0.007251	-0.015619	0.008922	0.029219	-0.014841	0.033924	0.029998	0.048442	-0.035021	-0.030544	-0.036464	-0.050744	-0.009806	-0.006807	0.017040	0.014977	-0.064259	0.006056	-0.010907	0.010907	-0.131622	0.032855	0.129834	-0.070428	-0.071205	-0.051835	-0.076328	0.062956	0.091649	-0.074088	-0.015633	-0.024442	-0.033249	-0.032604	0.063820	-0.098967	-0.069951	-0.043894	-0.024458	-0.031574	0.090113	0.013192	-0.054915	0.000541	-0.070863	-0.138224	-0.058370	-0.020000	-0.059310	-0.011968	-0.018923	0.071282	0.010927	0.112611	0.019271	-0.003188	0.004217	-0.009062	-0.005084	-0.011349	-0.125562	-0.015218	0.015703	-0.087971	-0.054583	0.010783	-0.073563	-0.031265	-0.056893	-0.100274	-0.092166	0.090696	0.103247	0.121331	0.114221	-0.131111	-0.085275	-0.057321	-0.107812	-0.027181	0.031465	-0.008132	-0.010690	0.014687	-0.008660	0.017324	0.004929	0.088525	0.015189	-0.071740	0.003571	-0.024810	-0.003426	-0.023455	-0.024342	-0.023602	-0.023602	0.001670	-0.046813	0.022416	-0.014797	0.006980	0.014992
O1_LONGITUDE	0.103847	0.049131	-0.116754	1.000000	0.722242	-0.059849	0.005890	0.040916	0.076138	-0.047052	0.092466	0.048002	0.054862	0.025678	-0.053052	-0.007246	0.111024	-0.043464	0.042924	0.026756	-0.052537	0.157427	-0.038824	-0.077794	0.021990	0.001056	0.158999	0.096881	0.123343	-0.017235	0.036344	0.156647	0.031219	0.063570	0.077015	0.059615	-0.069605	-0.055319	-0.015729	0.027118	-0.016016	0.019567	0.049462	-0.075790	-0.042502	0.087676	0.034470	0.106787	0.097361	0.133679	0.069490	0.018462	-0.085503	0.001610	-0.096979	0.047826	0.053204	0.148501	0.100278	0.050238	-0.029394	0.102556	0.054321	0.055253	0.073835	-0.038564	0.095919	0.128707	0.110858	0.064932	0.091128	0.101633	0.125377	0.020622	0.050030	0.044035	0.069107	-0.005397	0.034161	0.027586	0.060376	0.080412	0.027352	0.029976	0.029735	0.031818	0.007153	0.012391	0.045406	0.026420	0.041899	0.041899	0.036367	0.049831	0.044453	0.009573	0.024816	0.006541	0.000703	-0.007032	0.051872	0.029951	-0.060673	-0.183401	-0.041580	-0.061439	-0.025494	-0.043583	-0.088333	-0.090097	-0.075942	-0.067395	-0.043390	-0.069375	-0.033253	-0.033004	0.025731	0.030621	0.105126	0.022141	-0.098930	-0.059983	0.046854	0.039574	0.083208	0.048457	0.095436	0.095573	0.038452	-0.019340	-0.062291	0.093713	0.113991	-0.047929	0.048296	-0.119706	-0.004532	-0.070132	0.041083	-0.024455	-0.080336	-0.080137	-0.057402	0.148599	0.182667	0.003813	0.058313	0.125958	0.105388	0.110219	0.048692	0.088093	0.017225	0.088011	0.114540	0.025180	0.003396	0.172231	0.136028	0.067735	-0.079576	-0.037607	-0.018276	-0.119099	0.072351	-0.147336	0.088718	-0.062293	-0.021589	0.115776	0.166657	0.052051	0.046029	-0.058014	0.122322	-0.097843	0.055898	-0.024621	-0.001673	0.012886	0.009962	0.002914	0.099472	-0.016549	0.022025	-0.020535	0.001965	-0.017877	-0.061789	-0.134043	-0.103480	-0.095140	-0.132029	-0.120647	-0.096566	-0.040790	-0.087696	-0.085380	-0.106965	-0.060880	-0.159537	-0.140192	-0.172999	-0.157799	-0.171414	-0.169220	-0.192920	0.067324	-0.034810	-0.049142	0.039028	0.013217	0.013950	0.044254	0.068019	0.057231	-0.000463	-0.001730	-0.012831	0.146521	0.129855	0.112746	0.017245	0.022790	0.031638	0.104453	0.055863	0.072978	0.006945	0.114086	0.060013	0.006246	0.008200	-0.007043	-0.052169	-0.052169	-0.052169	0.116965	0.040416	-0.019283	-0.012083	0.096722	0.074968	-0.028549	-0.102809	0.070331	0.023407	-0.053729	0.205284	-0.090511	-0.011448	0.031098	-0.060291	-0.012437	0.020516	0.010807	-0.004067	-0.006007	-0.010135	-0.022925	0.030758	0.071994	-0.064756	-0.002962	0.072563	0.030148	0.004407	0.063031	0.095523	0.092697	0.043078	0.079558	0.078885	0.077140	0.000000	-0.028418	0.027831	0.030710	0.030255	-0.041624	-0.186009	-0.182453	-0.056417	-0.003473	0.109699	0.116146	0.117406	0.141208	0.086385	0.073428	0.073643	0.086589	-0.152446	-0.043940	0.024752	-0.033762	-0.016919	-0.100949	-0.032652	-0.078910	-0.090938	0.073038	0.089554	-0.055898	0.055898	0.001538	0.105672	0.036352	-0.043524	-0.116459	-0.057956	0.042890	0.062975	0.095493	-0.011585	-0.095488	-0.063279	0.155522	0.095380	0.045533	0.053513	-0.071893	-0.013968	-0.086525	-0.064361	-0.133592	-0.094198	0.078507	0.076468	-0.051110	-0.016485	0.080811	0.007910	0.006752	0.035578	0.048494	-0.006921	-0.055749	-0.013324	0.040492	0.024857	0.066688	-0.003209	0.069271	-0.039879	0.086671	-0.036644	-0.052619	0.001610	0.095523	0.063570	0.068971	0.054862	0.022025	0.099472	0.079998	-0.139070	-0.100646	-0.125564	-0.138864	0.020622	-0.005397	0.034161	0.017956	0.053052	0.043464	-0.077794	0.006317	0.027118	0.059615	-0.069605	0.007440	-0.120647	-0.040790	-0.087696	-0.113841	0.004226	0.029244	0.024145	0.021002	0.006185	0.006185	0.061703	0.025094	-0.025109	0.057092	0.060704	0.096979
O2_LATITUDE	-0.116227	-0.159549	0.306493	0.722242	1.000000	-0.057714	-0.048466	0.030617	0.026365	0.010427	0.018277	0.013106	0.015727	-0.021278	-0.019870	-0.022647	0.161237	-0.030911	0.044031	0.013569	-0.035372	0.076845	-0.006057	-0.033856	-0.032888	0.021261	0.081593	0.056054	0.085573	0.019782	-0.034462	0.104237	0.022075	0.063446	0.090255	0.071491	-0.033332	0.014179	-0.005217	0.080812	0.020683	0.051093	0.044425	-0.006098	-0.016498	0.048557	0.032853	0.089007	0.065746	0.062297	0.052924	0.037360	-0.079420	-0.008941	-0.058092	0.043269	0.012857	0.096540	0.046587	0.032090	-0.049095	0.032642	-0.015833	0.040282	0.010098	-0.003727	-0.005594	0.031509	0.045553	0.029078	0.054946	0.063292	0.082379	-0.020670	0.003755	0.027425	0.022470	-0.023306	0.030919	-0.011493	0.007841	0.028263	-0.008034	0.010184	-0.022825	-0.004155	0.022087	0.001762	0.031876	-0.008106	0.013948	0.013948	-0.009841	0.025440	0.034899	-0.011395	0.018948	0.021081	0.022163	0.026402	0.039985	0.087884	-0.093887	-0.116238	-0.052151	-0.068917	-0.064491	-0.064299	-0.068030	-0.083540	-0.057360	-0.043706	-0.057687	-0.051953	-0.021425	-0.048257	0.012493	-0.025869	0.089801	0.030490	-0.070871	-0.052063	0.041149	-0.045277	0.032087	0.050405	0.021414	0.030795	0.024061	-0.127352	-0.048963	0.105882	0.068656	-0.018468	0.081628	-0.065613	0.024147	0.013874	0.043830	-0.036618	-0.019808	-0.098844	-0.022182	0.089683	0.133593	0.002843	0.034406	0.097545	0.083250	0.089950	0.023429	0.064393	-0.016461	0.029382	0.087218	-0.006579	-0.002664	0.118318	0.110540	0.089574	0.008369	-0.025078	0.010500	-0.075447	0.077393	-0.061683	0.046905	-0.048148	-0.023386	0.016402	0.041475	0.043088	0.033111	0.003343	0.044711	-0.144469	0.051913	0.039219	0.027092	0.038489	0.046304	0.097639	-0.017156	-0.041290	-0.039234	-0.063902	0.012396	0.005655	0.009821	-0.061007	-0.036879	-0.006779	-0.025127	-0.030043	-0.018368	-0.041048	-0.136900	-0.021118	-0.046323	-0.026595	-0.097077	-0.141518	-0.095271	-0.073440	-0.108562	-0.107955	-0.134758	0.091358	-0.028428	-0.025500	-0.034123	-0.022712	-0.012131	-0.048510	0.013224	-0.011128	-0.035711	-0.066759	-0.054486	0.069473	0.045858	0.044780	-0.000801	0.020279	0.015235	0.061838	0.001122	0.061915	0.018295	0.076546	0.030728	-0.034531	-0.038919	-0.028224	-0.038414	-0.038414	-0.038414	0.076998	0.047612	-0.012989	0.083369	0.114979	0.073140	0.047906	-0.069170	0.066470	-0.021381	-0.028275	0.116606	0.004681	0.080791	0.018844	-0.066952	0.031385	0.028726	0.040409	-0.033413	-0.085949	-0.012566	-0.042934	0.038288	0.013065	-0.099399	-0.034321	0.029875	0.092969	0.074105	-0.030882	0.015356	-0.036587	-0.054650	-0.024572	-0.009361	-0.004602	0.000000	-0.020769	0.020958	0.027201	0.029743	-0.028287	-0.086147	-0.290074	-0.034140	-0.003928	0.067201	0.057421	0.022138	0.054537	0.037032	0.002902	0.028138	0.024046	-0.082991	-0.039814	-0.092469	-0.163632	-0.121836	-0.075421	-0.058499	-0.028549	-0.071411	-0.017564	0.054231	-0.051913	0.051913	-0.070618	0.116182	0.097418	-0.029474	-0.146316	-0.088151	0.008134	0.054544	0.073537	0.004754	-0.086937	-0.065128	0.067892	0.064300	0.064044	-0.028200	-0.114672	-0.026744	-0.065276	-0.061806	-0.070525	-0.052957	0.053965	0.053237	-0.092123	-0.053820	0.069863	0.028792	-0.035579	0.042097	0.058990	0.020303	-0.055008	0.049957	0.031032	0.018705	0.050419	0.007685	0.038677	-0.016867	-0.024675	-0.015254	-0.017894	-0.008941	0.015356	0.063446	0.024643	0.015727	-0.039234	-0.017156	-0.027479	-0.051794	0.003037	-0.012813	-0.026162	-0.020670	-0.023306	0.030919	-0.008039	0.019870	0.030911	-0.033856	0.006538	0.080812	0.071491	-0.033332	0.044793	-0.030043	-0.041048	-0.136900	-0.101487	0.010297	0.016982	0.017539	0.015430	-0.029156	-0.029156	0.037396	0.016643	-0.012550	0.033516	0.039279	0.058092
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Y001_2	-0.091389	-0.090997	-0.046813	0.025094	0.016643	0.063428	0.019789	0.132337	0.044242	0.025408	-0.049278	0.143255	0.168313	0.026434	0.019749	0.076745	0.029600	-0.120723	-0.093858	-0.081154	0.018967	0.113520	0.025399	0.027071	0.035880	-0.004006	0.006260	0.063080	0.119588	0.040729	0.030653	0.067890	0.002696	0.148260	0.112155	0.038635	-0.015322	0.087535	0.103538	0.061580	0.079453	0.067376	-0.015217	0.124271	0.194607	0.149493	0.158045	0.156920	0.195505	0.137440	-0.114491	-0.256509	0.054434	0.100253	0.035201	-0.044787	-0.112508	-0.099720	-0.021884	-0.192483	-0.117456	-0.145231	-0.121965	-0.137070	-0.050947	-0.175582	0.075986	0.020264	0.037274	-0.075929	-0.075766	-0.065082	-0.011072	0.049266	0.026325	0.059690	0.067607	0.061791	0.034226	0.068153	0.059792	0.062411	0.066859	0.081838	0.050388	0.046997	0.057224	0.110943	0.070478	0.085928	0.061468	0.061468	0.010447	0.093112	0.030717	-0.006200	0.009829	0.013945	0.018276	0.031186	0.015441	-0.035454	0.036683	-0.014390	-0.000418	0.027413	0.028147	0.030440	0.058288	0.067006	0.053873	0.039691	0.052461	0.074742	0.056996	0.040806	0.054845	-0.048373	-0.001657	0.082514	0.042155	0.036184	-0.018024	0.007062	0.108921	0.105169	0.109948	0.118744	0.031875	-0.028478	-0.004838	0.024223	0.116391	0.096292	0.062201	-0.018373	0.082355	-0.046521	0.009617	-0.135649	0.009585	-0.144752	-0.055729	-0.021749	-0.036591	-0.141356	-0.154521	0.004819	-0.038371	-0.089002	-0.049406	0.017080	-0.028910	0.040719	-0.029966	0.034374	-0.029987	0.074619	0.045042	-0.015815	-0.069374	-0.064073	0.043387	0.068626	0.038842	0.358239	-0.033686	0.196343	-0.059298	-0.077353	-0.056731	-0.015004	-0.022532	0.043422	-0.038338	-0.151016	0.076888	0.100832	0.141944	0.117464	0.113298	0.137052	0.069011	0.071861	0.002535	-0.091622	-0.015155	-0.016082	0.004428	0.025860	0.050675	0.102127	0.080114	0.105816	0.111074	0.088308	-0.044079	0.092925	0.108864	0.090367	0.035811	-0.047491	0.079873	0.104941	0.129818	0.055795	-0.064004	0.029527	-0.032192	-0.026692	-0.042081	0.004429	-0.196878	0.050488	-0.025545	-0.029979	-0.166636	-0.056847	-0.045624	-0.027233	-0.038936	-0.010272	-0.041127	-0.066759	-0.001779	-0.043159	-0.098500	-0.135199	0.020870	-0.098447	-0.168127	-0.170025	0.054586	0.032361	-0.007692	-0.007692	-0.007692	0.023774	-0.115416	-0.079483	-0.003472	0.072164	0.113293	0.018805	-0.111240	0.103774	0.081637	0.072020	-0.043247	-0.026671	0.018234	-0.060888	0.070720	-0.061791	-0.015806	-0.089231	0.024876	-0.067404	-0.143897	0.001534	-0.074222	-0.064329	-0.020025	0.014504	-0.089341	0.078439	0.041685	-0.028475	0.064506	0.114330	0.100256	0.090030	0.061293	0.017037	-0.041963	0.029728	-0.030708	-0.037916	-0.032647	0.026022	-0.020747	-0.060929	0.031534	-0.035624	0.051114	0.043886	0.035231	0.026624	0.109157	0.065950	0.118895	0.040221	0.027691	0.044968	-0.044228	-0.045498	-0.046676	0.017408	-0.008562	0.020592	-0.108104	0.037266	0.043586	-0.076888	0.076888	0.031312	0.045968	0.015818	0.013768	-0.051662	0.038906	0.035458	0.039204	0.030943	0.029906	-0.048844	-0.000611	0.021623	-0.030694	-0.055951	0.014598	-0.051070	0.002487	-0.104176	-0.009634	-0.008382	0.006443	0.071902	0.054432	0.114754	0.164731	0.099927	0.040694	0.073795	0.061840	-0.037609	0.078611	-0.019522	-0.010363	0.152678	0.116025	0.356955	0.507925	0.025800	0.130296	0.112968	0.284264	0.041551	0.100253	0.064506	0.148260	0.148920	0.168313	0.002535	0.069011	0.063537	0.034219	0.074349	0.059138	0.061665	0.049266	0.061791	0.034226	0.063844	-0.019749	0.120723	0.027071	0.039918	0.061580	0.038635	-0.015322	0.034356	0.105816	0.088308	-0.044079	0.064919	0.772435	0.100143	0.534206	0.532122	0.009767	0.009767	0.075052	1.000000	-0.226490	0.131468	0.047120	-0.035201
Y001_3	-0.001225	0.019165	0.022416	-0.025109	-0.012550	-0.064229	0.023036	0.031921	-0.068211	-0.077342	-0.011072	0.018946	0.015069	-0.022669	-0.073659	-0.073739	0.026942	-0.020951	0.038834	0.063403	0.037486	-0.060708	-0.005555	0.054936	0.033502	-0.014629	0.007146	0.025890	0.031732	0.000385	-0.007686	0.012294	-0.013807	-0.065446	-0.035466	-0.010100	-0.034609	-0.081064	-0.086399	-0.015689	-0.022618	-0.044360	-0.045807	-0.083277	-0.052140	-0.070279	0.032906	-0.028192	-0.081894	0.026132	0.037312	0.036292	-0.001792	-0.017982	-0.060032	0.017699	0.058977	0.056430	-0.001970	0.005081	-0.017050	0.038656	0.000748	0.012819	-0.063619	0.006075	-0.034932	0.071323	0.064879	0.048127	0.035112	0.034041	0.054384	-0.027351	0.033970	0.054055	0.022710	-0.034520	0.076224	0.009160	0.034645	0.009011	-0.017608	-0.021320	0.034911	0.052744	0.000984	-0.022568	0.002707	-0.021976	0.037489	0.037489	0.024737	0.042529	0.069755	0.078815	0.082162	0.052112	0.055029	0.029847	0.085013	0.099460	0.076523	0.035469	0.007328	0.037966	0.050826	0.030787	0.029681	0.055781	0.034471	0.039936	0.040578	0.047486	0.042941	0.079414	0.045216	0.050585	-0.012495	0.042996	-0.038668	-0.033042	-0.027400	0.161877	0.068050	0.046792	0.052881	0.037720	0.026993	0.059696	0.031785	-0.017022	-0.060523	0.051844	-0.036880	-0.034560	0.021461	0.053591	0.027919	-0.018738	-0.041430	-0.064675	0.060074	-0.051432	-0.056615	-0.096191	-0.055148	-0.121767	-0.034218	-0.058519	0.002341	-0.000948	0.015786	-0.079948	0.044160	-0.047721	0.035219	0.034413	0.077619	0.106007	-0.049841	-0.069430	-0.032878	-0.019585	0.045569	-0.038920	0.296133	-0.028258	-0.023439	0.092932	0.046399	0.049951	0.002607	-0.119958	0.057898	-0.054884	-0.056764	-0.001390	-0.032855	-0.043266	0.023448	0.073621	-0.065925	-0.013990	0.033534	0.071278	0.108026	-0.036065	0.056643	-0.051341	-0.007110	-0.041349	-0.042090	0.051986	0.034771	0.056003	-0.005580	0.092087	0.074502	0.070803	-0.092010	-0.107924	-0.019077	-0.009048	-0.018035	-0.041778	-0.032778	0.085964	0.160266	0.211696	-0.095349	-0.134629	-0.006811	-0.079482	-0.064583	-0.096408	-0.003174	-0.082349	-0.033829	-0.043901	-0.077744	-0.043512	-0.130850	-0.095486	-0.108772	-0.112929	-0.116091	0.021243	-0.126567	-0.083872	-0.022812	-0.077693	-0.026407	-0.017065	0.003051	0.003051	0.003051	0.090552	0.045761	-0.059515	-0.020068	0.040046	0.018093	-0.063641	-0.024479	-0.004037	-0.014834	-0.036442	-0.025847	0.050726	-0.012855	0.008063	-0.012923	0.007810	0.001551	0.066856	0.003584	0.024550	0.061189	0.028995	0.080540	0.000034	-0.007669	-0.050519	0.065830	-0.032752	-0.049417	0.055005	0.064964	0.032589	0.060934	0.021988	0.081971	0.084485	-0.014273	0.055878	-0.055864	-0.047452	-0.055882	-0.031061	0.050776	0.048065	0.034065	-0.067343	0.061214	0.057296	0.057430	0.060120	0.057225	0.015883	0.063633	0.024822	-0.030799	-0.041713	0.015390	-0.039673	-0.050493	0.009155	-0.030830	-0.062569	-0.054289	0.028213	0.030376	0.056764	-0.056764	0.062305	-0.036093	-0.043499	0.059705	0.035251	0.051477	0.003928	-0.009906	0.036914	0.068186	0.051795	0.052858	0.106206	-0.049190	0.032295	0.033354	0.025812	0.073422	-0.038018	-0.026188	-0.076706	-0.058834	-0.052557	0.049824	-0.062707	-0.035001	0.007624	0.037265	-0.045064	0.030933	0.051170	-0.029699	-0.029650	0.014263	0.041154	0.034107	0.426769	0.712358	0.018454	-0.044513	-0.007269	0.148600	0.006141	-0.017982	0.064964	-0.065446	-0.010822	0.015069	0.033534	-0.065925	-0.021967	-0.092654	-0.057851	-0.058985	-0.080376	-0.027351	-0.034520	0.076224	-0.004187	0.073659	0.020951	0.054936	0.079749	-0.015689	-0.010100	-0.034609	-0.028622	0.051986	0.056003	-0.005580	0.031689	0.408428	0.094019	0.278556	0.274487	0.099336	0.099336	0.110548	-0.226490	1.000000	0.029919	0.071426	0.060032
Y001_4	0.034508	0.069050	-0.014797	0.057092	0.033516	-0.054987	0.025147	0.098700	0.032423	0.084733	-0.009176	0.024442	0.001773	0.054244	0.078079	0.003514	0.057666	-0.022925	-0.097363	0.014082	0.103732	0.004875	0.028309	-0.082988	0.042101	-0.099095	-0.061193	0.013850	-0.012751	-0.061261	-0.007157	0.007364	-0.017454	0.102075	0.169151	-0.035013	-0.131060	0.009722	-0.021698	0.085339	0.129773	0.100342	0.038930	0.036189	0.102934	0.035460	0.184034	0.106826	0.107103	0.102102	0.065328	-0.003983	0.060148	0.033704	-0.021107	-0.005213	-0.046781	0.014926	0.030672	-0.139607	-0.001978	-0.047063	0.022359	0.027700	-0.001023	-0.058826	0.135428	0.014000	0.060302	-0.002001	0.023202	0.023713	0.066823	0.095480	0.076087	0.074710	0.057040	0.090255	0.071558	0.069534	0.019384	0.059768	0.079608	0.046849	0.054559	0.042225	0.121523	0.112648	0.027570	0.097928	0.053766	0.053766	0.051918	0.054580	0.056495	0.100965	0.092350	0.096878	0.105480	0.104437	0.065420	0.152712	-0.128537	-0.027347	-0.031633	0.212100	0.191937	0.170006	0.176856	0.188089	0.155935	0.189856	0.215817	0.196417	0.179640	0.206456	0.159483	0.067427	0.160386	0.156386	0.002775	-0.021895	-0.163940	0.158923	-0.012991	0.015031	-0.027989	0.075202	0.069009	0.036529	0.141015	0.111467	0.189446	0.184367	0.164790	0.009961	0.194062	-0.105061	0.198869	-0.078258	0.020072	0.003412	-0.092283	0.021449	0.065438	-0.104665	-0.097833	0.120076	0.131171	0.143013	0.054758	0.060745	0.042358	0.000494	0.002634	-0.008193	0.031690	-0.006142	0.026276	-0.065328	-0.042810	-0.122840	0.093767	0.123994	0.102529	-0.100149	0.100488	0.418647	-0.016653	0.043769	-0.000351	0.075148	0.170210	0.036202	0.031322	-0.011137	0.070905	0.080621	0.023541	0.050840	0.039315	-0.016960	0.013707	0.020597	0.011758	-0.090763	0.006788	-0.136448	0.114231	0.033819	0.096396	0.134784	0.114284	0.015144	0.029187	0.061798	-0.047895	-0.012851	0.009908	0.027001	0.038862	-0.076613	0.114794	0.092652	0.027416	0.120848	-0.114297	0.150813	0.117121	0.139366	-0.087161	0.028618	-0.092599	-0.033162	-0.091229	-0.088549	-0.150038	-0.020041	0.108700	0.120997	0.033121	0.130822	-0.006375	-0.032234	0.029921	-0.022805	-0.043342	-0.126484	0.007506	-0.011199	-0.140658	-0.086730	-0.064886	-0.017050	0.084137	0.084137	0.084137	0.100387	-0.063990	0.001629	0.139143	0.065642	0.000465	0.060983	-0.151101	0.110916	0.064316	0.073361	0.042371	-0.015965	-0.004439	-0.137599	0.092837	-0.142789	0.092339	0.000637	0.121000	-0.119063	-0.181260	0.127041	-0.129041	-0.038127	0.088676	-0.023344	-0.016759	0.112280	0.136937	-0.075259	0.098930	0.082626	0.078472	0.089600	0.006650	-0.012053	-0.006834	0.053302	-0.053807	-0.048174	-0.059283	0.001543	0.073464	0.018229	0.046956	-0.070653	0.168287	0.186729	0.185778	0.183033	0.055447	0.056765	0.047120	0.048712	-0.032445	0.071619	0.001140	0.084131	0.000226	-0.020137	-0.001681	-0.036643	-0.080546	0.146302	0.070490	-0.070905	0.070905	-0.109988	0.052585	0.005171	-0.060692	-0.061695	-0.030089	-0.076550	0.055011	0.082130	-0.063144	-0.118282	-0.034927	0.034732	-0.045217	-0.049092	-0.050817	-0.090998	-0.062405	-0.066303	-0.074255	-0.030055	0.022373	0.019735	0.025449	0.144322	0.014792	0.005510	-0.103427	0.048366	-0.093540	-0.053909	0.149558	-0.017539	0.032863	0.107090	0.090330	0.441540	0.112737	-0.116778	0.142375	0.057430	0.021215	-0.002545	0.033704	0.098930	0.102075	0.099334	0.001773	0.011758	0.013707	0.008960	0.057860	0.123373	0.099611	0.099382	0.095480	0.090255	0.071558	0.103424	-0.078079	0.022925	-0.082988	-0.085991	0.085339	-0.035013	-0.131060	-0.007407	0.015144	0.061798	-0.047895	0.014804	0.132258	0.085703	0.151286	0.149358	0.058981	0.058981	0.069053	0.131468	0.029919	1.000000	-0.133847	0.021107
Y001_5	0.050213	0.051057	0.006980	0.060704	0.039279	-0.110445	0.014154	0.088687	-0.080449	0.033175	0.015593	0.084457	0.034484	-0.008512	-0.018393	0.042288	0.073328	-0.075002	-0.080648	-0.018821	-0.004480	0.061466	-0.031232	0.028268	0.034340	0.087661	-0.001612	0.218186	0.087273	0.076078	0.110971	0.161025	0.144545	0.004353	0.014973	-0.000057	-0.028249	-0.060493	0.025817	-0.080272	-0.041181	0.050411	0.107519	0.004154	0.032271	0.073419	-0.009143	0.019278	0.067165	0.140596	0.040134	-0.093154	-0.036839	-0.083242	0.049152	0.003294	-0.024027	0.031768	-0.029391	-0.030447	-0.138858	-0.129140	-0.119680	-0.090002	-0.050497	-0.121516	0.037051	-0.005841	-0.033017	-0.064324	-0.096023	-0.100525	-0.072666	-0.144370	-0.034035	-0.000376	0.026928	-0.116118	0.003221	-0.041792	0.009485	-0.010591	-0.082528	-0.045313	-0.030670	-0.022580	-0.104529	-0.100057	0.005333	-0.076493	0.048023	0.048023	-0.014030	0.012701	0.017460	0.015138	0.004997	0.003240	0.013370	0.016667	-0.061898	0.019205	0.127795	-0.008391	-0.070941	-0.048938	-0.042230	-0.045050	-0.085759	-0.093648	-0.100818	-0.098137	-0.075000	-0.087485	-0.077256	-0.062826	-0.091358	-0.115994	0.041770	0.140833	0.095384	0.001872	0.053208	-0.022044	0.070185	0.027529	0.057145	-0.024462	-0.048477	0.072199	-0.022625	0.001738	0.073835	0.121512	-0.024224	-0.171230	-0.053767	-0.073615	-0.022852	-0.209755	-0.129468	-0.148065	-0.077990	0.095491	-0.081894	-0.244132	-0.179793	-0.083072	-0.126765	-0.067329	-0.027348	0.037692	-0.041719	-0.033413	-0.096565	-0.027805	-0.057522	0.079249	0.044529	-0.029335	0.030011	0.059419	0.064059	-0.074181	0.091261	0.018292	-0.052559	-0.159964	-0.185876	-0.030329	0.025594	0.105012	0.016229	0.039408	-0.152310	-0.132401	0.038006	0.011962	0.019061	0.002446	0.027975	0.033452	-0.117822	0.027623	0.025111	0.007386	-0.015321	0.093004	-0.054771	-0.125825	-0.037021	-0.028121	-0.011250	-0.004664	-0.005325	0.027434	-0.073482	0.054113	-0.024573	0.038803	-0.030200	-0.100305	-0.025711	-0.027886	0.005123	0.004592	-0.111880	0.112129	-0.027374	0.003069	-0.063756	-0.140156	-0.078109	-0.050873	-0.096903	-0.186522	-0.078723	-0.164885	-0.110163	-0.155358	-0.064071	-0.040731	-0.088688	-0.094912	-0.163952	-0.123988	-0.234676	-0.104933	-0.114854	-0.085634	-0.126952	-0.194751	0.017124	-0.006225	0.029148	0.029148	0.029148	-0.006212	-0.165202	-0.112630	-0.057337	0.054221	-0.020606	-0.070536	-0.141710	0.012786	-0.090821	-0.029628	-0.039820	-0.130140	-0.067614	0.055265	-0.043444	-0.071464	-0.002792	-0.077999	0.035681	0.010512	-0.038570	-0.037781	0.090125	0.002283	0.069321	-0.046052	-0.107334	-0.007099	-0.027832	-0.005870	-0.056171	0.030167	0.054739	0.103838	0.156804	0.134344	-0.010723	0.016510	-0.015898	-0.023512	-0.021796	0.003470	0.007949	0.057649	-0.010714	-0.012688	-0.015578	-0.027568	-0.028968	-0.000821	0.043213	0.011277	0.025391	0.023832	-0.057775	-0.071666	0.102017	-0.006793	-0.018050	0.086864	-0.003717	-0.040152	-0.088569	-0.015872	0.010226	-0.038006	0.038006	0.060411	0.018289	0.047693	0.026227	-0.020474	0.050132	0.026207	-0.001335	0.015419	0.014197	0.048693	0.039906	0.075697	0.022034	-0.001801	-0.053188	0.001315	0.000758	-0.042557	-0.022558	-0.077851	-0.087605	0.002994	0.009183	-0.054874	0.007470	0.115186	0.033879	0.029796	0.002816	0.066941	0.015521	-0.062323	0.094161	0.048208	0.039398	0.510287	0.092670	0.041361	-0.090747	-0.058880	0.033555	-0.073462	-0.083242	-0.056171	0.004353	-0.074812	0.034484	0.025111	-0.117822	-0.044825	-0.074576	-0.020118	0.004114	-0.043403	-0.144370	-0.116118	0.003221	-0.110067	0.018393	0.075002	0.028268	0.055699	-0.080272	-0.000057	-0.028249	-0.058791	-0.004664	0.027434	-0.073482	-0.051194	0.086353	0.173891	0.189131	0.181195	0.050613	0.050613	0.201818	0.047120	0.071426	-0.133847	1.000000	-0.049152
inverse_Q46	0.014024	0.062452	0.014992	0.096979	0.058092	-0.095931	-0.048116	-0.159143	-0.009166	-0.098175	-0.006396	-0.110458	-0.100021	-0.081338	0.088650	-0.054504	-0.006127	0.037876	-0.104678	0.098798	0.012205	-0.032973	0.031293	-0.027708	-0.115408	-0.035484	-0.119962	0.088275	-0.084350	-0.004251	-0.125888	-0.107315	-0.025515	-0.183220	-0.027955	0.010701	-0.008951	0.007038	-0.051793	-0.091603	-0.094733	-0.109046	-0.099815	-0.056819	0.003923	-0.052690	-0.090227	-0.099327	-0.047731	0.000789	0.090330	-0.051395	-0.124204	-0.120704	-1.000000	-0.506641	0.394343	0.452586	0.437768	0.231129	0.183272	0.247042	0.270797	0.254855	-0.178191	0.044243	-0.137760	-0.035540	-0.047881	-0.015329	0.037204	0.029844	-0.103714	-0.114536	-0.121256	-0.180421	-0.243370	-0.232350	-0.270218	-0.257286	-0.237262	-0.208465	-0.211129	-0.215194	-0.196201	-0.193136	-0.192700	-0.168162	-0.162786	-0.178915	-0.254598	-0.254598	-0.157023	-0.208507	-0.131726	-0.141758	-0.108001	-0.127248	-0.159950	-0.159749	-0.080765	0.085346	-0.181671	-0.088351	0.213212	0.159728	0.162999	0.165941	0.175990	0.150442	0.162634	0.191268	0.162640	0.152556	0.191768	0.174159	0.060624	-0.023760	0.085727	0.069182	-0.043823	-0.072357	-0.072599	-0.013600	-0.163120	-0.133605	-0.149615	-0.081816	0.015255	-0.085785	0.043968	0.119652	0.100123	0.100021	0.014936	-0.030363	-0.068993	-0.095306	0.089246	0.014142	-0.007740	-0.034804	-0.244736	0.079449	0.088937	-0.037193	-0.025467	0.101966	0.075410	0.148830	0.184983	0.106245	0.125834	0.085045	0.017994	0.077589	0.070525	-0.018331	0.027789	0.011589	0.046322	-0.001062	0.050459	0.079597	0.010310	-0.056850	0.010583	0.021225	-0.013845	0.014479	0.049011	-0.024942	-0.022126	-0.045399	0.127793	0.051494	-0.104830	-0.024766	-0.086139	-0.101104	-0.018608	-0.050666	-0.123326	-0.249817	-0.051300	-0.108654	-0.063817	0.010085	0.157914	0.100443	0.158784	0.165261	0.173135	0.107073	0.089848	0.122731	0.078026	0.049802	0.041711	0.114761	0.071713	0.107160	0.095730	0.118097	0.060079	0.109843	0.045462	0.030511	0.065211	0.055923	-0.106264	-0.052481	0.014434	-0.114474	0.008317	-0.071121	-0.025401	-0.084789	-0.011783	-0.046816	0.102219	0.135313	0.065140	0.093234	-0.008263	0.022737	0.067534	0.024737	0.053420	0.145039	0.056314	0.051497	-0.087746	-0.050565	0.032178	0.032178	0.032178	-0.105752	-0.091330	0.005807	0.030534	-0.012749	-0.093698	0.060803	0.006232	-0.020109	-0.136754	0.009690	-0.072711	-0.100927	0.008263	-0.116761	0.040610	0.062022	0.062394	-0.083867	0.077211	-0.049650	-0.033015	0.123466	-0.076049	-0.065433	-0.035668	-0.075386	-0.062635	0.217538	0.211310	-0.200452	-0.157584	-0.113625	-0.101864	-0.117548	-0.148084	-0.154017	-0.007892	0.009988	-0.009220	0.005827	-0.010803	-0.041248	0.013486	0.008016	-0.049639	-0.008400	0.010114	0.008855	0.062732	0.080278	-0.014954	-0.008787	-0.048202	-0.058225	-0.011242	0.045207	-0.011875	-0.023238	0.039029	0.045872	-0.011174	-0.160974	-0.159930	0.181902	0.141091	0.104830	-0.104830	-0.186400	-0.024012	-0.024179	-0.162109	-0.004253	-0.066400	-0.236504	-0.033812	0.024783	-0.151453	-0.085806	-0.136667	0.024074	0.097765	0.117778	0.011317	0.044963	0.021078	-0.199087	-0.166550	0.094412	0.068449	-0.086511	-0.061913	0.148449	-0.066853	-0.066540	-0.101730	0.100793	-0.146378	-0.159326	0.173989	0.073093	0.182750	0.077746	0.080116	0.016728	0.035161	-0.068548	-0.079057	-0.183210	0.013232	0.066722	-0.120704	-0.157584	-0.183220	-0.206931	-0.100021	-0.051300	-0.123326	-0.118664	0.093862	0.156234	0.155969	0.149807	-0.114536	-0.232350	-0.270218	-0.243467	-0.088650	-0.037876	-0.027708	-0.081595	-0.091603	0.010701	-0.008951	-0.033973	0.107073	0.122731	0.078026	0.128965	0.012887	0.031659	0.042118	0.035144	0.007938	0.007938	0.028458	-0.035201	0.060032	0.021107	-0.049152	1.000000